# Locate Hong Kong talk notes > for my (final version of my slides and in my talk transcript) I gave a 'section 4' of my generating philosophy paper. the talk in hong kong. can you tell me all that was said/on the slides (+ notes as well actually). give me an obsidian link to the note please. > > /btw actually, is there a way of making you and claude always give me the obsidian vault link rather than me having to ask for it to be revealed in finder. <details><summary>12 previous messages</summary> > I’ll use the recall and Obsidian CLI guidance here, because you’re asking for a specific past talk/slide artifact and an Obsidian note link rather than a general memory-sketch. I’ll also treat the “always give vault link” question as a cross-runtime config question, so I’ll inspect the current Claude/Codex instructions before saying what’s possible. > > <details><summary>Explored 4 files</summary> > > - Read `./.codex/skills/file-management/SKILL.md` > - Read `./.codex/skills/cross-runtime-config/SKILL.md` > - Read `./.codex/skills/recall/SKILL.md` > - Read `./.codex/skills/obsidian-cli/SKILL.md` > > </details> > > I’m going to search the generated conversation archive first, then I’ll read the actual note or deck file once I find the artifact. For the config question, I’m checking the operational vault because this touches Claude/Codex behavior rather than just the philosophy vault. > > <details><summary>Ran 4 commands</summary> > > - Ran `qmd search "Hong Kong generating philosophy slides" -n 10` > - Ran `obsidian search query="generating philosophy Hong Kong" limit=20` > - Ran `qmd search "section 4 generating philosophy" -n 10` > - Ran `qmd search "generating philosophy section 4 Hong Kong slides talk transcript" -n 10 -c conversations` > > </details> > > The Obsidian CLI’s `search` command is unavailable in this setup, so I’m staying inside Obsidian via `eval` for the vault search and using QMD for the archive. The early hits point toward the Generating Philosophy session file, but I still need the talk/slides artifact itself. > > <details><summary>Ran 5 commands</summary> > > - Ran `obsidian eval code="const qs=['hong kong','section 4','generating philosophy']; const files=app.vault.getMarkdownFiles(); const out=[]; for (const f of files){const text=await app.vault.cachedRead(f); const low=text.toLowerCase(); if(qs.every(q=>low.includes(q))) out.push({path:f.path,mtime:f.stat.mtime,name:f.name});} JSON.stringify(out.sort((a,b)=>b.mtime-a.mtime).slice(0,50))"` > - Ran `qmd search "section 4 talk transcript slides notes" -n 20 -c notes` > - Ran `qmd search "Generating Philosophy Hong Kong transcript slides notes" -n 20 -c notes` > - Ran `qmd search "Hong Kong talk transcript generating philosophy" -n 20 -c notes` > - Ran `obsidian eval code="(async()=>{const qs=['hong kong','section 4','generating philosophy']; const out=[]; for (const f of app.vault.getMarkdownFiles()){const text=await app.vault.cachedRead(f); const low=text.toLowerCase(); if(qs.every(q=>low.includes(q))) out.push({path:f.path,mtime:f.stat.mtime,name:f.name});} return JSON.stringify(out.sort((a,b)=>b.mtime-a.mtime).slice(0,80));})()"` > > </details> > > One vault-wide content scan hit an Obsidian size limit, so I’m narrowing the search to likely project/talk paths and excluding huge imported book notes. The useful QMD hit so far is the 14 Apr cleaned transcript, which may be adjacent rather than the slide artifact itself. > > <details><summary>Explored 1 search, 2 lists, ran 1 command</summary> > > - Ran `obsidian eval code="(async()=>{const qs=['hong kong','generating philosophy']; const roots=['Notes/','Sessions/','Writing/','Talks/','Slides/','Presentations/']; const out=[]; for (const f of app.vault.getMarkdownFiles().filter(f=>roots.some(r=>f.path.startsWith(r))&&f.stat.size<1000000)){const text=await app.vault.cachedRead(f); const low=text.toLowerCase(); if(qs.every(q=>low.includes(q))) out.push({path:f.path,mtime:f.stat.mtime,size:f.stat.size,name:f.name});} return JSON.stringify(out.sort((a,b)=>b.mtime-a.mtime).slice(0,100));})()"` > > <details><summary>Explored 1 search, 2 lists</summary> > > - Listed files > - Searched for `Hong Kong|section 4|Section 4` in `Notes` > - Listed files > > </details> > > </details> > > Found it: `Notes/Generating Philosophy — Talk Transcript (Lingnan–Genoa–Kobe, 2026-04-23).md`. I’m reading that now, then I’ll identify what counts as Section 4 across spoken transcript, slide text, and speaker notes. > > <details><summary>Explored 4 files, 6 searches, 1 list, ran 1 command</summary> > > - Ran `obsidian read path="Notes/Generating Philosophy — Talk Transcript (Lingnan–Genoa–Kobe, 2026-04-23).md"` > > <details><summary>Explored 6 files, 6 searches, 1 list</summary> > > - Searched for `^## |^### |Section 4|Where are the great` in `Generating Philosophy — Talk Transcript (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Searched for `Where are the great|Section 4|great LLM texts|slide` in `Generating Philosophy with AI — Argument Moves (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Searched for `^## |^### |Section 4|Where are the great` in `Generating Philosophy — Talk Speaker Notes (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Read `./Notes/Generating Philosophy — Talk Speaker Notes (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Read `./Notes/Generating Philosophy with AI — Argument Moves (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Read `./Notes/Generating Philosophy — Talk Transcript (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Searched for `^# 2[5-9]|§4|Where are the great|distill|Sellars|specialised` in `Generating Philosophy — Talk Speaker Notes (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Read `./Notes/Generating Philosophy — Talk Speaker Notes (Lingnan–Genoa–Kobe, 2026-04-23).md` > - Searched for `Coda|simplest diagnosis|distillate|specialist LLM|In sum|§4` in `generating-philosophy-audience-deck-FINAL.html` > - Listed files in `generating-philosophy-talk` > - Searched for `Coda|simplest diagnosis|distillate|specialist LLM|In sum|§4` in `generating-philosophy-speaker-deck-FINAL.html` > - Read `./Attachments/generating-philosophy-talk/generating-philosophy-speaker-deck-FINAL.html` > - Read `./Attachments/generating-philosophy-talk/generating-philosophy-speaker-deck-FINAL.html` > > </details> > > </details> > > For Section 4 I now have three layers: the actual transcript, the speaker-note plan, and the final speaker deck HTML. I’m doing one quick config check now so I can answer the “can this be automatic?” part from the actual current setup instead of guessing. > > <details><summary>Explored 1 file, ran 2 commands</summary> > > - Ran `h="$HOME" > sed -n '1,220p' "$h/.claude/knowledge/system-design-practices.md"` > > <details><summary>Explored 1 file</summary> > > - Read `./Agent State Vault/START HERE.md` > > </details> > > - Ran `h="$HOME" > rg -n "Obsidian|vault link|finder|Finder|obsidian://|clickable file links|file links" "$h/.claude" "$h/.codex" AGENTS.md` > > </details> </details> Found it. The note is: [Generating Philosophy — Talk Transcript](obsidian://open?vault=My%20Obsidian%20Vault&file=Notes%2FGenerating%20Philosophy%20%E2%80%94%20Talk%20Transcript%20%28Lingnan%E2%80%93Genoa%E2%80%93Kobe%2C%202026-04-23%29). Companion notes: [Talk Speaker Notes](obsidian://open?vault=My%20Obsidian%20Vault&file=Notes%2FGenerating%20Philosophy%20%E2%80%94%20Talk%20Speaker%20Notes%20%28Lingnan%E2%80%93Genoa%E2%80%93Kobe%2C%202026-04-23%29). ### Section 4 Section 4 was titled “Where are the great LLM texts?” in the transcript, and “§4 · a speculative coda” in the final deck/speaker notes. It began around slide 25, scheduled for 08:31. What you said: - You framed §4 as more tentative: §§1–3 had given reasons to think LLMs can produce philosophy worth reading, but the obvious challenge is: if so, where are the great LLM texts now? - You said the issue is not obviously the capacity of LLMs, but our knowledge of how to extract the relevant capacities. - You contrasted generic prompts such as “explain the Mary argument” with worthwhile philosophy: generic prompts produce flat, clichéd, hedged, survey-style prose. - You returned to the corpus-as-distillate idea: the philosophical corpus is the result of many rounds of argument, counter-argument, survival, citation, teaching, and uptake. LLMs inherit that distillate statistically, but they do not perform the distillation in a single completion. - You suggested that philosopher-prompters may need to elicit good philosophy by using argument and explanation markers: “so,” “therefore,” “this is a clear objection,” and similar terms. - You ended with the specialist-vs-generalist LLM thought. You invoked Sellars’s “how things, in the broadest sense, hang together” line to suggest that philosophy may be better served by broad general-purpose models than by narrowly specialised philosophy models. On the slides / speaker notes: - Slide 25: “§4 · a speculative coda.” On-screen question: if the capacity §§1–3 argue for is there, why are we not seeing more LLM-produced philosophy worth reading? - Slide 26: “what the prompt asks for.” The simple diagnosis: the LLM’s output depends on what it is asked for. Generic prompts invite generic survey prose; good papers require a specific problem, position, and opposition. - Slide 27: “distillate, not distillation.” The corpus is the distillate, not the distillation. The philosopher’s role is to supply iteration in the prompt: draft, press, redraft. - Slide 28: “a specialist LLM?” The Sellars quote was on the slide. The note explicitly says the claim is tentative: a general LLM may be more valuable to philosophy than a specialist one because philosophy concerns maximal breadth. - Slide 29: “In sum.” The §4 summary line was: what the LLM does not itself perform, in any single completion, is the iteration that produced the corpus; that is where the philosopher’s work now sits. And yes, there is a way to make us always give you Obsidian links. The clean shared rule is to add something to `/Users/nickyoung/AGENTS.md` saying: when referencing a note in the main vault, include an `obsidian://open` link, not only a Finder/path link. Since this is a shared behavior for Claude and Codex, it belongs in shared instructions; if it keeps failing, then Claude can additionally get a hook or skill reminder, while Codex can get the closest native equivalent in its global/project instructions. *La sezione 4 trattava il divario tra la capacità filosofica ereditata dal corpus e il lavoro iterativo necessario per farla emergere in un testo leggibile.* > i wanted more of a topic based overview Yes, sorry, I gave you the archive map more than the conceptual map. Topic-wise, Section 4 was doing this: ### 1. The “where are the great LLM texts?” problem The section starts from an obvious pressure on the whole talk. If §§1–3 are right, and there is no constitutive or capacity barrier to LLM-produced philosophy, then why aren’t we already surrounded by excellent LLM philosophy papers? You were not denying the force of that question. You treated it as the practical problem left over once the in-principle objections have been answered. ### 2. Bad prompts produce bad philosophy You then diagnosed much existing LLM philosophy as a prompting problem. If people ask generic philosophical questions, they get generic philosophical prose: summaries, balanced surveys, “on the one hand/on the other hand” prose, clichés, possibly plagiarism-like regurgitation. The contrast was: worthwhile philosophy is not “write about free will” or “explain Mary.” It is a specific argument, for a specific conclusion, against specific alternatives, under pressure from objections. ### 3. The corpus as distillate This was the major conceptual image. The philosophical corpus is not just a pile of texts. It is the residue of a historical filtering process: arguments answered by counterarguments, papers taken up or ignored, views refined through criticism, weak work dropping out of attention, stronger work surviving. So the LLM inherits the distillate: patterns of objection-handling, explanation, comparison, argumentative structure. But it does not, in one completion, perform the distillation that produced those patterns. ### 4. Prompting as surrogate iteration That leads to the positive practical claim: the philosopher’s work may now lie in supplying the missing iteration. The prompt-session has to reproduce, in compressed form, something like the discipline’s historical process: draft, object, revise, sharpen, test, redraft. So “prompting” was not treated as merely giving instructions. It was closer to staging a dialectical process that forces the model to use the philosophical patterns it has inherited. ### 5. Argumentative cueing You also floated the idea that prompts should exploit the verbal machinery of philosophy: “therefore,” “so,” “one might object,” “the stronger reply is,” “this leaves us with,” and so on. These are not just stylistic tics; they are traces of argumentative operations. The idea was that philosopher-prompters may need to elicit the right discourse-level patterns rather than ask for “answers.” ### 6. Generalist rather than specialist models The final topic was whether we should train specialised philosophy LLMs. You suggested some resistance to that thought, using Sellars: philosophy aims to understand how things “in the broadest possible sense” hang together. So the speculative Sellarsian thought was: maybe philosophy benefits from generality. A broad model, trained across many domains of human description, may be better suited to philosophy than a narrowed “philosophy-only” model, because philosophy itself is a broad hanging-together activity. ### 7. The role of the philosopher The section ends with a revised role for the human philosopher. The philosopher is not needed because the text must be authored by a person in order to count as philosophy. Rather, the philosopher is needed practically because good LLM philosophy may require disciplined elicitation, selection, objection, and iteration. So the human role migrates from “the sole producer of the philosophical text” to “the agent who makes the distillation happen in the interaction.” Obsidian link again: [Generating Philosophy — Talk Transcript](obsidian://open?vault=My%20Obsidian%20Vault&file=Notes%2FGenerating%20Philosophy%20%E2%80%94%20Talk%20Transcript%20%28Lingnan%E2%80%93Genoa%E2%80%93Kobe%2C%202026-04-23%29). *La sezione 4 non chiudeva l’argomento, ma trasformava la domanda teorica in una domanda pratica su come far emergere filosofia buona dal modello.* > aint no obsidian link > > User context > > Images: > - `./Screenshots/Screenshot 2026-05-04 at 18.54.39.png` Yep, fair. I gave you a Markdown link whose target was an `obsidian://` URL, but in this interface it clearly doesn’t surface as the obvious vault link you wanted. Here’s the raw link: ```text obsidian://open?vault=My%20Obsidian%20Vault&file=Notes%2FGenerating%20Philosophy%20%E2%80%94%20Talk%20Transcript%20%28Lingnan%E2%80%93Genoa%E2%80%93Kobe%2C%202026-04-23%29 ``` And the note title is: ```text [[Generating Philosophy — Talk Transcript (Lingnan–Genoa–Kobe, 2026-04-23)]] ``` For the “make Claude/Codex always do this” thing: yes, the rule should be “when referencing an Obsidian note, always include both the wikilink and the raw `obsidian://open?...` URI.” Markdown-hiding the URI is too easy to miss or get swallowed by the client. The most robust version is to update the shared `AGENTS.md` plus Claude’s instruction surface so both agents emit raw Obsidian URIs by default. *Il collegamento deve essere visibile come oggetto pratico, non nascosto dietro una formattazione che l’interfaccia può inghiottire.* > have a look at the longform projeect. in particular section 2. I would like to explore how useful that would be in terms of talking about good verses bad prompting <details><summary>14 previous messages</summary> > I’ll look at the Generating Philosophy session file and the longform Section 2 itself, then I’ll treat your question as exploratory: not “write the paper now,” but “what affordances does Section 2 give us for thinking about good versus bad prompting?” > > <details><summary>Explored 1 file, 1 list, ran 1 command</summary> > > <details><summary>Explored 1 file, 1 list</summary> > > - Read `./Sessions/Generating Philosophy.md` > - Listed files in `generating-philosophy-text-internal-evaluation` > > </details> > > - Ran `obsidian eval code="const tag='generating-philosophy'; JSON.stringify(app.vault.getMarkdownFiles().filter(f=>{let fm=app.metadataCache.getFileCache(f)?.frontmatter; let tags=fm?.tags; if(typeof tags === 'string') tags=[tags]; return Array.isArray(tags) && tags.includes(tag) && f.path.startsWith('Notes/')}).map(f=>({path:f.path,mtime:f.stat.mtime,tags:app.metadataCache.getFileCache(f)?.frontmatter?.tags||[]})).sort((a,b)=>b.mtime-a.mtime).slice(0,20))"` > > </details> > > The project note says Section 2 was reworked around Floridi plus the virtue-filtered corpus thesis, with voice issues still flagged. I also found later Section 2 move notes from 27 April, so I’m going to read both the longform scene and the recent §2 response note rather than treating the draft file as the whole state of play. > > <details><summary>Explored 4 files</summary> > > - Read `./Notes/Generating Philosophy — §2 Response (Product-Centred, 2026-04-27 v2).md` > - Read `./Writing/research/generating-philosophy-text-internal-evaluation/2. The challenge from abduction.md` > - Read `./Notes/Generating Philosophy — Revised Section 2 Moves (Lipton Two-Filter Version).md` > - Read `./Writing/research/generating-philosophy-text-internal-evaluation/2. LLMs and Abduction.md` > > </details> > > There are two live Section 2 shapes. The longform scene has the raw content and a lot of your inline anger at its structure; the April moves note is much cleaner and explicitly says the philosopher-prompter supplies “present-tense direction and selection.” That looks directly useful for the good/bad prompting question, but I’m checking Section 4 too so I don’t accidentally import something §4 already owns. > > <details><summary>Explored 1 file, 1 search</summary> > > - Searched for `prompt|prompter|distill|generation|selection|live candidates|candidate` in `generating-philosophy-text-internal-evaluation` > - Read `./Writing/research/generating-philosophy-text-internal-evaluation/4. Finding Virtue in Text.md` > > </details> > > I’ve got enough now. One housekeeping thing from your project instructions: because we loaded the Generating Philosophy project today, I’m updating its `last-worked` date to 2026-05-05, then I’ll give you the conceptual read. > > <details><summary>Edited 1 file</summary> > > - Wrote `./My Obsidian Vault/Sessions/Generating Philosophy.md` (+1 -1) > > </details> > > Yes. Having looked at the longform project, I think Section 2 is very useful for the good-versus-bad prompting issue, but not mainly because of the current prose in `2. LLMs and Abduction.md`. The cleaner resource is the later Section 2 moves note, especially the Lipton two-filter version. > > Raw Obsidian links: > > ```text > obsidian://open?vault=My%20Obsidian%20Vault&file=Writing%2Fresearch%2Fgenerating-philosophy-text-internal-evaluation%2F2.%20The%20challenge%20from%20abduction > ``` > > ```text > obsidian://open?vault=My%20Obsidian%20Vault&file=Notes%2FGenerating%20Philosophy%20%E2%80%94%20Revised%20Section%202%20Moves%20%28Lipton%20Two-Filter%20Version%29 > ``` > > ### How Section 2 Helps > > The useful thought is: bad prompting asks the model to produce philosophy while withholding the very structure that makes philosophical generation possible. It says something like “write about free will” or “explain Mary,” so the model generates the most probable continuation in a broad public-explainer region: summary, balance, hedging, familiar positions, no pressure. > > Good prompting, by contrast, recreates something like Lipton’s two filters. > > First, it shapes generation. It does not ask the model to roam the whole space of possible things one might say. It gives a problem, a contrast, a live candidate, a background, an objection, or a pressure point. In Section 2 terms: it helps form the shortlist of live options. > > Second, it supplies selection. It does not treat the first fluent continuation as the answer. It presses: which candidate explains more, which distinction is doing work, where is the ad hoc move, what objection survives, what would make this position stronger? In Section 2 terms: the philosopher-prompter reintroduces present-tense evaluation. > > So the good/bad prompting distinction could be framed like this: > > Bad prompting delegates both generation and selection to the model. Good prompting distributes them across corpus, model, prompt, and philosopher-reader. > > That is much stronger than just saying “write better prompts.” It gives the prompting section a philosophical account of why some prompts fail. A bad prompt is not merely vague; it places the model in the wrong candidate-space and gives it no selection pressure. A good prompt is not merely detailed; it constructs a local dialectical situation in which the likely continuation is also a philosophically assessable move. > > ### The Section 2 → Section 4 Relation > > I’d treat Section 2 as the theoretical basis and Section 4 as the practical development. > > Section 2 says: Floridi is right that the model does not itself perform abduction. But abductive structure can be distributed. The corpus contains residues of past generation and selection; the model learns that structure; the prompt directs generation; the philosopher-reader selects and iterates. > > Section 4 can then ask: what does this mean for prompting? > > That gives you a nice distinction between: > > - bad prompting: asking for an answer without a problem-space > - better prompting: asking for a move within a dialectic > - best prompting: iterative prompting that generates, objects, revises, and selects > > The current Section 4 already has this in rough form: dialectical framing, solution-gestured prompting, conversational iteration. But Section 2 gives it philosophical depth. It lets you say that prompting is not a mere practical afterthought. It is the way the missing abductive filter gets reintroduced. > > ### Possible Topic Structure > > A topic-based version of this part might go: > > 1. Why generic prompts produce bad philosophy > They activate survey prose, not philosophical argument. > > 2. Why this is not evidence of incapacity > The model has inherited a structured philosophical background, but that background is not automatically expressed. > > 3. What good prompts do > They specify live candidates, contrasts, objections, standards of success, and the direction of pressure. > > 4. What the philosopher contributes > Not authorship in the old sense, but selection: rejecting weak continuations, sharpening the dialectic, forcing the model through further rounds. > > 5. What this shows about LLM philosophy > The relevant unit is not “model alone produces paper,” but a human-LLM practice in which the model contributes candidate philosophical moves and the philosopher supplies direction and assessment. > > I also updated the project session note’s `last-worked` date to `2026-05-05`. > > *La distinzione tra un prompt povero e uno buono non riguarda la quantità di istruzioni, ma il tipo di spazio filosofico che il prompt apre e disciplina.* > > the april moves is way out of date, and i am not convinced you are referring to the right version of section 2 > > You’re right to call that out. I over-weighted the April moves note because it looked polished and recent, but if it is out of date then it should not be steering the read; I’m going back to the longform index and project metadata to identify which Section 2 file is actually live. > > <details><summary>Explored 1 file, 1 search, 1 list, ran 1 command</summary> > > <details><summary>Explored 1 file</summary> > > - Read `./Writing/research/generating-philosophy-text-internal-evaluation/Index.md` > > </details> > > - Ran `obsidian eval code="const root='Writing/research/generating-philosophy-text-internal-evaluation/'; JSON.stringify(app.vault.getFiles().filter(f=>f.path.startsWith(root)).map(f=>({path:f.path,mtime:f.stat.mtime,size:f.stat.size})).sort((a,b)=>b.mtime-a.mtime))"` > > <details><summary>Explored 1 search, 1 list</summary> > > - Searched for `*json` in `generating-philosophy-text-internal-evaluation` > - Listed files in `generating-philosophy-text-internal-evaluation` > > </details> > > </details> > > You’re right. I was not looking at the right evidence hierarchy. > > The longform `Index.md` currently says the live scenes are: > > ```text > 0. Introduction > 1. The Challenge from Authorship > 2. Finding Virtue in Text > References > ``` > > and it explicitly ignores: > > ```text > 3. LLMs and Abduction > ``` > > So the April moves note should not have been treated as live, and neither should the old `2. LLMs and Abduction.md`. The more recent file is: > > ```text > obsidian://open?vault=My%20Obsidian%20Vault&file=Writing%2Fresearch%2Fgenerating-philosophy-text-internal-evaluation%2F2.%20The%20challenge%20from%20abduction > ``` > > But there is a wrinkle: `2. The challenge from abduction.md` is recently modified and looks like the current Section 2 draft, while the Longform index does not include it in `scenes`. So either the index is out of sync, or Section 2 is currently outside the compiled longform sequence. That matters. > > Reading that newer Section 2, the useful prompting thought is a bit different from what I said before. It is not primarily “good prompting recreates Lipton’s two filters” in a neat ready-made way. It is more like this: > > Section 2 argues that Floridi’s challenge concerns the process: the LLM does not knowingly generate rival explanations and select the best. The reply is product-facing: an LLM text can still present a potential explanation, set it against rivals, and exhibit virtues like unity, simplicity, non-ad-hocness, and explanatory power. > > That gives a sharper way to talk about prompting. Bad prompting asks for an answer, and so gets an answer-shaped product whose philosophical status is hard to assess. Good prompting asks for a candidate explanation under specified pressure. It makes the output legible as a philosophical product: what is the candidate, what are its rivals, what does it explain, what cost does it incur, what objection is it meant to survive? > > So the prompting distinction could be: > > Bad prompt: “Explain X” or “write about X.” > This invites a fluent continuation, but it does not build the conditions under which the output can be judged as a philosophical move. > > Good prompt: “Given problem P, and rival explanations A and B, develop candidate C only if it explains E better without ad hoc cost; then state the strongest objection.” > This does not make the model an abductive reasoner. It makes the generated text assessable as a potential explanation. > > That is the route I’d explore from the current Section 2. Prompting is useful not because it magically installs reasoning in the model, but because it shapes the generated product into something that can enter ordinary philosophical assessment. > > Also: I should not have leaned on the April moves note. Treat that as stale unless you explicitly revive it. > > *Il punto non è che il prompt trasformi il modello in un ragionatore, ma che renda il testo prodotto valutabile come una mossa filosofica.* > > obsidian://open?vault=My%20Obsidian%20Vault&file=Writing%2Fresearch%2Fgenerating-philosophy-text-internal-evaluation%2F2.%20The%20challenge%20from%20abduction this one for fucks sake </details> Yes. That one. Sorry. I’ve got it now: ```text obsidian://open?vault=My%20Obsidian%20Vault&file=Writing%2Fresearch%2Fgenerating-philosophy-text-internal-evaluation%2F2.%20The%20challenge%20from%20abduction ``` For that version, the useful connection to good versus bad prompting is this: Section 2 is not saying “prompting supplies the missing abductive reasoning.” It is saying something subtler: Floridi is right that the model does not perform inference to the best explanation as a mental process. But a generated text can still contain a potential explanation, and potential explanations are exactly the kinds of things philosophy assesses. So, in prompting terms: Bad prompting asks the model for “an explanation” or “a discussion” and then receives fluent prose whose philosophical role is unclear. It may sound explanatory, but there is no specified contrast, no live rival, no clear explanandum, no cost structure, no pressure from objections. Good prompting makes the output into a candidate explanation. It gives the model a philosophical task shaped like the thing Section 2 says we can assess: here is the phenomenon, here are the rival views, here is what needs explaining, here is the pressure, now produce a candidate that can be judged for simplicity, strength, unity, non-ad-hocness, and explanatory power. The section’s useful thought is therefore product-centred: prompting matters because it determines whether the generated text is merely answer-shaped or whether it has the structure of an assessable philosophical candidate. A good prompt does not prove that the model reasoned. It produces something whose philosophical quality can be tested by reading. That also lets Section 4 avoid sounding like “prompt engineering tips.” The prompting discussion can be grounded in Section 2’s metaphilosophy: good prompts create the conditions under which LLM output can enter the ordinary economy of philosophical assessment. *Il prompt buono non crea una mente abduttiva, ma costruisce un testo che può essere trattato come una spiegazione candidata.* > you are relally not being very helpful I wanted to brainstorm about how i can use our response to the challenge of section 2 in answering questions of prompting and how to get good philosophy worthwhile philosophy out of the LLM. You’re right. I got stuck in file-identification mode and then gave you compressed summaries instead of actually thinking with you. Let me reset. The way to use Section 2 for the prompting question is not: “Section 2 already gives us a theory of prompting.” It doesn’t. It gives us a response to Floridi that can be extended into a theory of prompting. The Section 2 response, as I understand it, is: Floridi says LLMs do not perform abduction. They generate plausible continuations without understanding the problem, generating rivals, or selecting the explanation that best accounts for the data. The reply is: fine, but philosophical evaluation need not ask whether the producer performed abduction. It can ask whether the produced text contains a potential explanation, whether it sets up live alternatives, whether it exhibits virtues like simplicity, strength, unity, non-ad-hocness, and explanatory illumination. That is already very close to a theory of good prompting, because it tells us what the prompt has to make possible. A bad prompt asks for output in a form where those virtues are hard to test. “Write about the Mary argument.” “Explain whether LLMs can do philosophy.” “Give me arguments for and against X.” These prompts invite survey prose. The model can satisfy them by producing something balanced, familiar, fluent, and useless. The output may contain explanation-like sentences, but it is not forced to present a candidate explanation under pressure. A good prompt, by contrast, should make the model produce something that can be assessed in the way Section 2 says philosophical texts are assessed. It should force the output to have a target, rivals, costs, and pressure. In other words, the prompt should not ask the model to “do philosophy” in the abstract. It should ask the model to produce a candidate philosophical move. So the connection might be: Section 2 answers Floridi by relocating abduction from the producer’s inner process to the assessable structure of the product. The prompting section then says: good prompting is the practice of eliciting products with that assessable structure. That feels like the bridge. The really useful distinction might be between “prompting for content” and “prompting for philosophical role.” Prompting for content says: tell me about physicalism, Mary, AI authorship, free will. Prompting for philosophical role says: formulate the objection; identify the live rival; make the distinction do work; explain what the view explains that its rivals do not; show the cost of denying this premise; repair the argument without adding an ad hoc clause. That is much more interesting than “be specific in your prompts.” The problem with bad prompting is not merely that it is vague. It is that it fails to assign the generated text a role inside inquiry. Maybe there are three levels: 1. Topic prompts “Write about X.” These produce encyclopedia prose, summaries, and familiar dialectical furniture. Mostly bad for worthwhile philosophy. 2. Position prompts “Defend view V about X.” Better, because the model now has a direction. But still often bad, because it can defend V by listing standard considerations without making a live move. 3. Dialectical-role prompts “Given this pressure on V, produce the strongest non-ad-hoc repair; compare it with rival R; say what explanatory gain is bought and what cost is incurred.” This is where Section 2 becomes active. The model is being asked to produce a potential explanation or argumentative candidate with assessable virtues. The core thought: worthwhile LLM philosophy comes not from asking for philosophical subject matter, but from prompting the model into a philosophical function. That gives you a nice answer to the “where are the great LLM texts?” question too. They are not appearing because most users are prompting at level 1. They ask the model to continue from a generic philosophical topic, and the likely continuation of a generic philosophical topic is generic philosophical exposition. To get good philosophy, the prompt has to locate the model inside a structured problem-space. Another useful formulation: The model is good at continuation. The prompter’s job is to make the right continuation be a philosophical achievement. This links directly to Section 2. If Floridi is right, the model is always producing continuations. The question is: continuation of what? If the prompt is a generic request, the continuation is generic prose. If the prompt is a sharply staged philosophical problem, the continuation can be a candidate move in that problem. The prompter does not change the mechanism. The prompter changes what the mechanism is continuing. That might be the most elegant way to frame it: Floridi: LLMs only generate plausible continuations. Reply: then the philosophical question becomes what they are continuing. Bad prompts give them non-philosophical beginnings: topics, summaries, requests for balance. Good prompts give them philosophical beginnings: tensions, contrasts, explanatory burdens, objections, partial arguments. The output is worthwhile when the most plausible continuation of that beginning is itself a worthwhile philosophical move. That is a genuinely strong use of Section 2. It also avoids overselling prompting. You do not need to say “good prompting makes the LLM reason.” You can say: even if the process remains mere continuation, a prompt can structure the continuation-space so that the resulting product is a candidate explanation, objection, distinction, or repair. Then the philosopher evaluates it. So maybe Section 4 becomes something like: If Section 2 is right, the practical problem is not how to make LLMs abductively reason. It is how to write prompts whose continuations have the structure of abductive philosophical products. That could then break into practical types: - Explanandum prompts: specify what needs explaining. - Contrast prompts: specify rival explanations or positions. - Cost prompts: ask what must be given up to preserve the view. - Objection prompts: force the candidate to survive pressure. - Repair prompts: ask for a non-ad-hoc modification. - Selection prompts: compare candidates by simplicity, strength, unity, and explanatory gain. - Iteration prompts: repeat the cycle until the output stops improving or the cost becomes too high. This would be a much more philosophically interesting taxonomy than “prompt clearly, give context, iterate.” And it gives the paper a powerful self-referential shape. The paper can say: our own use of LLMs works when we do not ask “write Section 2.” It works when we ask: here is Floridi’s challenge, here is our concession, here is Lipton’s distinction, here is the pressure, now find the move that preserves product-level assessment without pretending the model reasons. That is exactly prompting as philosophical role-assignment. So the slogan might be: Good prompting does not ask the model for philosophy. It gives the model a place in a philosophical dialectic. Or: The prompt is not an instruction manual; it is the opening of a dialectical situation. Or more soberly: Prompting matters because it fixes the philosophical role of the continuation. That last one sounds paper-useful. *Il prompt non deve chiedere al modello di pensare al posto nostro, ma deve costruire una situazione in cui la continuazione possa diventare una mossa filosofica valutabile.* > [$contemplate](./.codex/skills/contemplate/SKILL.md) Why you give me fucking slogans? I'm not a child. > > And I wasn't talking about this stuff. You're being fucking stupid. I'm talking about all of the more technical stuff regarding how LLMs train and how philosophy is working in the corpus that's ingested, etc. etc. Can you apply yourself a bit please? You're really not Yeah, you're making it like pulling teeth at the moment make sure that you explain your analysis, justification, reasons etc etc. for your answer BEFORE giving me your final answer. You’re right. I was being stupid in the exact way that is most annoying here: I turned a technical-metaphilosophical question into “prompt better by giving the model a role.” That is thin, and it misses what you were actually asking about: how the Section 2 response to Floridi, especially the stuff about training, learned distributions, philosophical corpora, and abductive structure encoded in writing, can explain why some prompting gets worthwhile philosophy out of an LLM and some does not. I can’t give you private chain-of-thought in the literal stream-of-consciousness format the skill asks for, but I can give you the thing you actually need: the analysis, assumptions, inferential steps, and reasons before the conclusion. ## Analysis Start from Section 2’s actual pressure point. Floridi’s challenge is not merely “LLMs sometimes give bad answers.” It is a mechanistic challenge: the system is trained to predict continuations. It does not begin with a phenomenon, represent it as needing explanation, generate rival hypotheses, compare them under explanatory virtues, and select the best. So if philosophy requires that kind of abductive activity, then LLMs are missing something. Your response in Section 2 is not to deny the mechanism. That is important. You grant that the model is doing next-token prediction, not human-style abductive inference. The answer is instead that the training distribution is not neutral. The model is not sampling continuations from an arbitrary heap of sentences. It has been trained on a corpus in which reasoning, criticism, objection, reply, distinction-making, and theory comparison have already been deposited in writing. That is the technical hinge for prompting. The prompt is not just an instruction. It is a conditioning context. It changes which learned regularities become relevant to the next continuation. A bad prompt conditions the model on a generic task type: “explain X,” “write about Y,” “give pros and cons.” Given that context, the most probable continuations are likely to come from the broad distribution of educational summaries, encyclopedic exposition, blog-style balance, introductory teaching prose, and safe assistant-answer prose. Those genres may mention philosophy, but they are not where the corpus most strongly encodes philosophical evaluation. A good prompt, in the technical sense, conditions the model on a different region of the learned distribution: the region where philosophical writing is doing work under pressure. It supplies enough of the local dialectical structure that the likely continuation is drawn from patterns learned from actual philosophical practice: “here is the target phenomenon,” “here is the rival,” “here is the objection,” “here is why the existing reply is unstable,” “continue by finding a non-ad-hoc repair,” “show what cost the repair incurs.” That kind of prompt is not better because it is longer. It is better because it resembles the contexts in which good philosophical continuations occur in the training corpus. So the Section 2 answer can become a theory of prompting by saying this: If an LLM has absorbed philosophical structure only as statistical structure, then prompting is the art of making the relevant structure conditional on the present context. That is not a slogan; it is the technical relation. The model’s competence is distributional. Prompting is distributional steering. Bad prompting fails because it activates the wrong learned genre. Good prompting works because it makes the continuation problem resemble the continuation problems embedded in the philosophical corpus. This also helps with the “good philosophy” question. Worthwhile philosophy is not just any text about philosophical subject matter. It is text that bears the marks of having passed through pressure: it handles alternatives, acknowledges costs, avoids arbitrary repairs, clarifies why a distinction matters, explains something that needed explaining. If the training corpus contains many such pressure-shaped sequences, then an LLM may learn them without understanding them as norms. But the prompt has to make those sequences relevant. This gives you a more technical distinction between bad and good prompting: Bad prompting is under-conditioned with respect to philosophical structure. It gives the model a topic but not the local constraints that, in the corpus, distinguish philosophical work from exposition. Good prompting is philosophically structured conditioning. It gives the model a partial argumentative state such that the next plausible continuation is one that must answer to philosophical constraints. This also explains why “reasoning mode” or “think step by step” is not the heart of the matter. If Floridi is right, adding hidden tokens does not by itself turn the model into an abductive reasoner. It may improve search through continuation-space, but what matters for philosophy is whether that search is conditioned by the right material. A long chain produced from a bad initial framing can just elaborate bad survey prose. Conversely, a sharply conditioned prompt can make even a relatively short completion do real philosophical work. So Section 2’s response to Floridi gives you a pretty serious account of prompting: 1. LLMs do not internally perform abductive selection. 2. But they are trained on written products of abductive and dialectical selection. 3. Those products encode philosophical standards as statistical regularities. 4. A prompt conditions which regularities govern the continuation. 5. Good prompts make pressure-shaped philosophical regularities relevant. 6. Bad prompts leave the model in generic exposition or assistant-answer space. 7. The philosopher’s role is therefore not only to ask, but to construct the conditioning context in which worthwhile continuation becomes probable, and then to evaluate the result. The nice thing is that this stays faithful to Floridi. You do not need to say: “Actually the model reasons.” You say: “Floridi is right about the process. But the process operates over distributions shaped by human reasoning. Prompting matters because it determines whether the output draws on those reasoning-shaped distributions in a way that yields an assessable philosophical product.” That also helps with the corpus point. The philosophical corpus matters not only because it contains good conclusions. It contains patterned transitions: objection to reply, problem to distinction, counterexample to restriction, theory to cost, explanatory demand to candidate explanation. LLMs are continuation machines. So what they can learn from philosophy is precisely these transition patterns. Good prompting gives them the right preceding state for such a transition. That is probably the route you want. ## Final Answer The Section 2 response can support a much more technical account of prompting than the one I was giving. The core idea is: because the LLM learns from a corpus in which philosophical reasoning has been expressed as sequences of text, good prompting is not mainly “clear instruction.” It is the construction of a context that makes the next-token task resemble the kinds of transitions found in serious philosophical writing. Bad prompting gives the model a topic. The model then draws on generic patterns associated with that topic: summaries, surveys, familiar objections, balanced exposition. This is why it produces stuff that is fluent but not worthwhile. Good prompting gives the model a partially developed philosophical situation: a phenomenon to explain, live rivals, a pressure point, a failed reply, a cost to avoid, a standard of adequacy. That makes the likely continuation draw on the corpus’s pressure-shaped argumentative patterns rather than on generic expository patterns. So the Section 2 connection is: Floridi is right that the model does not perform abduction. But the model has learned from texts shaped by human abduction. Prompting is the technique for conditioning the model so that those learned abductive and dialectical structures become relevant to the continuation. That means the prompting section should not be framed as practical advice. It should be framed as the practical consequence of the Section 2 metaphysics of LLM output: - The corpus stores the public traces of philosophical reasoning. - The model learns those traces as statistical regularities. - The prompt selects which regularities are brought to bear. - Worthwhile LLM philosophy emerges when the prompt makes the continuation-space philosophical in the relevant sense: constrained by explanation, comparison, objection, repair, and cost. The human contribution is not simply “writing a good prompt.” It is building the local philosophical environment in which the model’s learned continuation patterns can produce something worth assessing. Raw link to the live Section 2 file: ```text obsidian://open?vault=My%20Obsidian%20Vault&file=Writing%2Fresearch%2Fgenerating-philosophy-text-internal-evaluation%2F2.%20The%20challenge%20from%20abduction ``` *Il punto tecnico è che il prompt non aggiunge ragionamento al modello, ma determina quali tracce statistiche del ragionamento umano diventano operative nella continuazione.* > i didn't ask for actual chain of thought, i asked you to answer me in a particular style. You’re right. I overcorrected into a policy disclaimer when you were asking for a style of answer: analysis and justification first, then the answer. Sorry. Let me do this properly. ## Analysis Before Answer The useful starting point is not “prompting gives the model a role.” That is too thin. The useful starting point is Section 2’s technical concession to Floridi. Section 2 says: yes, the model is not doing abduction as a mental operation. It is not representing hypotheses as hypotheses, comparing them, and selecting one because it best explains the data. It is producing continuations from a learned distribution. But that does not settle whether the output can have philosophical value, because the distribution has been shaped by texts in which abductive and dialectical work has already been done. So the crucial object is not the model considered in isolation. It is the relation between: - the corpus the model ingested, - the statistical structure learned from that corpus, - the prompt as a conditioning context, - the continuation generated under that context, - and the philosopher’s assessment of the output. That gives us a more serious way of thinking about prompting. A prompt is not just a request. It is the local textual environment from which the model continues. Since the model works by learned continuation, the prompt determines which parts of the learned distribution become live. If the prompt resembles a generic educational query, the model draws on the region of the corpus shaped by summaries, explainers, encyclopedic answers, and bland compare-and-contrast writing. If the prompt resembles the middle of a serious philosophical dialectic, the model draws on patterns associated with objection, repair, contrast, qualification, theoretical cost, and explanatory gain. This means bad prompting is not bad because it is short or vague in some everyday sense. It is bad because it under-specifies the philosophical situation. It fails to reproduce the kinds of constraints under which worthwhile philosophical writing is produced in the corpus. Good prompting, by contrast, is good because it conditions the model on the right kind of prior text: not a topic, but a structured philosophical pressure. It gives the model enough of the dialectical position that the most plausible continuation is not a survey but a move. The Section 2 response therefore gives you a technical account of why prompting matters. If the model had an internal abductive faculty, prompting might just be asking it to exercise that faculty. But if Floridi is right, and the model has no such faculty, then prompting becomes much more important. Prompting is how we get from the model’s general learned distribution to the subset of continuations shaped by philosophical reasoning. So the relevant bridge from Section 2 to prompting is: Since LLMs do not themselves perform abduction, the philosophical work must be located in the interaction between learned corpus structure and prompt-conditioned continuation. That is a much stronger claim than “ask better questions.” ## Answer I think the most promising use of Section 2 is to make prompting a consequence of the paper’s response to Floridi. Floridi says: the model only produces plausible continuations. It does not abductively reason. Your reply can be: exactly. So if we want worthwhile philosophy from such a system, the question is how to make the plausible continuation of a prompt also be a philosophically valuable continuation. That depends on the training corpus. The model has seen philosophical writing in which certain transitions recur: from objection to reply, from counterexample to distinction, from explanatory demand to candidate theory, from cost to revision, from rival views to comparative assessment. These are not empty stylistic patterns. They are the written traces of philosophical reasoning. The model learns them statistically rather than normatively, but it can still reproduce them in the right context. Prompting is the act of creating that right context. So the good/bad prompting distinction could be developed like this: Bad prompting gives the model philosophical subject matter without philosophical structure. It asks “What is consciousness?” or “Explain the Mary argument.” The model’s learned distribution contains vast amounts of generic expository prose following prompts like that, so it produces summary, balance, qualification, and familiar textbook dialectic. That output may be accurate enough, but it is not likely to be worthwhile philosophy. Good prompting gives the model a structured philosophical situation. It says, in effect: here is the explanandum; here are the live rivals; here is the objection; here is why the obvious reply fails; now continue by finding a non-ad-hoc repair, or by showing which rival best explains the relevant data. This pushes the model into the part of its learned distribution shaped by actual philosophical work. So Section 2 lets you say that worthwhile prompting has to supply at least some of the structure that abduction would otherwise supply internally: - It narrows the space of live candidates. - It fixes the explanatory target. - It specifies the relevant rivals. - It marks the standards of success. - It introduces objection and cost. - It creates pressure for non-ad-hoc continuation. - It leaves the output open to assessment by the philosopher. That last point matters. The prompt does not make the LLM into a reasoner. It makes the output into something that can be assessed as a candidate philosophical move. The philosopher then performs the selection Floridi says the model lacks: rejecting weak continuations, pressing objections, asking for repair, comparing rival outputs, and deciding whether the resulting text actually explains anything. This gives Section 4 a real argumentative basis. It would not be a practical appendix about “prompt engineering.” It would be the practical upshot of the Section 2 reply: If LLMs inherit philosophical reasoning only as learned structure in text, then good prompting is the method for activating and testing that inherited structure. That also explains why “where are the great LLM texts?” is not a decisive objection. Most uses of LLMs do not create the right conditioning context. They ask for philosophical content, not for a continuation inside a live philosophical problem. The absence of great texts from bad prompting practices tells us little about what the model can produce under philosophically structured conditions. A possible section-level claim might be: The value of prompting is not that it smuggles human reasoning into the output and thereby makes the model irrelevant. Nor is it that it causes the model to reason in the human way. Prompting matters because it determines which learned patterns from the philosophical corpus govern the continuation. A philosophically serious prompt is one that makes the most natural continuation answerable to the same virtues by which philosophical texts are ordinarily assessed. That seems to me the real bridge from Section 2 to the prompting question. *La questione non è come chiedere meglio, ma come costruire un contesto in cui la continuazione erediti le forme di pressione razionale presenti nel corpus filosofico.* > "Bad prompting gives the model philosophical subject matter without philosophical structure. It asks “What is consciousness?” or “Explain the Mary argument.” The model’s learned distribution contains vast amounts of generic expository prose following prompts like that, so it produces summary, balance, qualification, and familiar textbook dialectic. That output may be accurate enough, but it is not likely to be worthwhile philosophy. > > Good prompting gives the model a structured philosophical situation. It says, in effect: here is the explanandum; here are the live rivals; here is the objection; here is why the obvious reply fails; now continue by finding a non-ad-hoc repair, or by showing which rival best explains the relevant data. This pushes the model into the part of its learned distribution shaped by actual philosophical work." good, but i would like your answer to more closely connected with the actual techniacl work sone in section 2 ## Analysis Yes, the better version has to be tied much more tightly to what Section 2 is actually doing. The Section 2 argument has several technical pieces, and each one can be converted into a claim about prompting. First, Section 2 starts from Floridi et al.’s account of “zeroth-order abduction.” The LLM produces a plausible continuation with explanatory form, but it does not select that continuation by comparing hypotheses. So if prompting is going to matter, it cannot matter by making the model suddenly perform abduction internally. That would contradict the concession Section 2 makes. Prompting has to matter in some other way. Second, Section 2 uses Lipton’s account of potential explanation. This is important. Lipton says abductive inquiry does not begin with actual explanations, because then inquiry would already be over. It begins with potential explanations: candidates that would explain the data if true. This gives the prompting discussion a precise target. A good prompt is one that elicits potential explanations in Lipton’s sense, not just explanation-like prose. Third, Section 2 brings in Williamson’s account of philosophical abduction: theories are assessed by intrinsic virtues such as simplicity, strength, unity, informativeness, and non-ad-hocness. These are not hidden mental episodes. They are features of a theory as articulated. That means a good prompt should not merely ask for an answer; it should elicit an output in which those virtues, or failures of those virtues, can be inspected. Fourth, Section 2 says that the philosophical corpus is not a neutral heap of sentences. It is the written record of philosophical practice. It contains patterns of candidate generation, comparison, objection, revision, and preservation. The model learns transition probabilities over that material. So prompting matters because the prompt determines which learned transitions become relevant. Fifth, Section 2’s “trajectory” thought matters. The generated text is an extended continuation, not a single proposition. A philosophical argument is a structured trajectory through a space of possible continuations. So a good prompt is not just one that names a topic, but one that sets the initial conditions for a trajectory likely to pass through philosophically relevant transitions: objection, distinction, rival comparison, cost-accounting, repair. That gives a more technically faithful account. ## Answer The Section 2-based version should say something like this. Floridi’s challenge is that LLMs produce plausible continuations without abductive selection. They do not generate rival hypotheses, compare them as hypotheses, and select one because it best explains the data. Section 2 grants this. The question is therefore not how prompting can turn stochastic continuation into inner abduction. The question is how prompting can make stochastic continuation yield the kind of product that philosophical abduction evaluates. That is where Lipton enters. On Lipton’s picture, inquiry works with potential explanations: candidates that would explain the data if true. A bad prompt often fails because it does not ask the model to produce a potential explanation in this sense. “Explain the Mary argument” asks for expository continuation. It does not fix a phenomenon to be explained, specify candidate explanations, or require comparison among them. So the model gives explanation-like prose without producing a candidate whose explanatory credentials are assessable. A good prompt, by contrast, asks for an output that can function as a Liptonian potential explanation. It supplies the explanandum, the contrast class, and the rival candidates, or at least enough of them for the continuation to be constrained by explanatory comparison. It asks not “what is X?” but “which of these candidate accounts would, if true, make this pattern intelligible, and at what theoretical cost?” That connects directly with Section 2’s use of Lipton. Williamson then adds the standards by which the prompted output should be shaped. If philosophical theories are assessed by simplicity with strength, non-ad-hocness, unity, informativeness, and explanatory power, then a good prompt should force those features into view. For example, it can ask the model to repair a view without adding an ad hoc exception; to compare two candidates by what each explains; to identify what a distinction buys; to state the cost of preserving a premise; to show whether a proposed reply increases unity or merely patches a counterexample. So the contrast is not simply: ```text bad = vague good = specific ``` It is: ```text bad = gives topic without abductive structure good = gives conditions under which the output can be assessed as a potential explanation ``` The corpus point deepens this. Section 2 argues that the model has been trained on philosophical writing where these transitions already occur: objection to reply, rival to comparison, pressure to distinction, theory to cost, failed account to repair. The model has not learned them as norms it understands, but as regularities in philosophical text. Prompting is the way of conditioning the model so that these regularities, rather than generic expository regularities, govern the continuation. So “What is consciousness?” pulls from a distribution rich in introductory exposition. “Here is a problem for higher-order theories of consciousness; here is why the standard reply looks ad hoc; produce a repair that preserves explanatory strength without multiplying machinery” pulls from a different region of the learned distribution: one shaped by the kinds of transitions that occur inside philosophical argument. That is the technical connection to Section 2. Since the LLM is a continuation engine, the practical question becomes: what continuation are we asking it to produce? If the prompt begins with a generic topic, the plausible continuation is generic prose. If the prompt begins with an abductively structured philosophical situation, the plausible continuation may be a candidate explanation, distinction, objection, or repair that can be evaluated by the standards Section 2 identifies. This also preserves the product-versus-producer distinction. The prompt does not make the model’s process abductive. It makes the product more likely to contain the public structure of abduction: a candidate explanation, live alternatives, comparative pressure, and visible theoretical cost. The philosopher then assesses whether the generated trajectory actually has the virtues Williamson names and whether the potential explanation is lovely in Lipton’s sense. Raw link: ```text obsidian://open?vault=My%20Obsidian%20Vault&file=Writing%2Fresearch%2Fgenerating-philosophy-text-internal-evaluation%2F2.%20The%20challenge%20from%20abduction ``` *Un prompt filosoficamente serio non chiede soltanto un tema, ma prepara le condizioni perché il testo prodotto possa funzionare come spiegazione candidata.* > maybe this won't be in the text itself but you are still not giving me what i want, i want a more technically grounded account as to how the models are trained and operate works in this contet ## Analysis Right. The thing I should have been talking about is not “good prompts give structure” in the abstract, but the actual mechanism by which a transformer trained on large text corpora can be sensitive to philosophical structure at all. So let me start from the technical picture and then connect it back to the Section 2 response. An LLM is trained, initially, on a next-token prediction task. The training objective is brutally simple: given a sequence of tokens, adjust the model’s parameters so that it assigns higher probability to the token that actually came next in the training data. Repeated across an enormous corpus, this forces the model to internalise regularities that help predict continuations. Some of these regularities are low-level: spelling, syntax, collocation. Some are mid-level: genre, register, paragraph shape, citation practices, argumentative markers. Some are high-level: what tends to follow when a paper introduces an objection, how authors usually repair a theory after a counterexample, what kinds of distinctions tend to be treated as legitimate rather than arbitrary, what it looks like for a view to be compared with a rival. The important point is that the model does not need to represent these as explicit rules in order to become sensitive to them. It does not need a stored proposition like “non-ad-hocness is a philosophical virtue.” It only needs its parameters to be adjusted so that continuations exhibiting non-ad-hoc repair are more probable in contexts where the corpus contains such repairs. The norm is learned as a pattern of continuation, not as a norm. That is where philosophy is special in the Section 2 argument. Philosophical reasoning is unusually text-saturated. The public object of the discipline is not merely a report of prior reasoning; the reasoning is laid out in the text. Objection, reply, contrast, concession, theory-choice, counterexample, disambiguation, cost-accounting: these are all carried by sequences of words. So when a model is trained on philosophy, it is not just absorbing conclusions. It is absorbing the textual forms in which philosophical comparison and selection are publicly performed. Now add the fact that training data is not an unstructured heap. Even if the model is not trained only on peer-reviewed articles, high-quality philosophical writing in books, articles, syllabi, encyclopedias, lecture notes, reviews, and discussions has already been shaped by disciplinary selection. This is not a perfect filter. It is noisy, prestige-laden, historically contingent, biased, full of mediocre material. But it is still not random. The texts that are repeatedly cited, taught, summarized, archived, discussed, and copied into downstream corpora tend to be texts that have survived some amount of philosophical pressure. That means the model is exposed not only to philosophy as subject matter, but to a distribution already enriched for certain argumentative forms. This is the corpus-side point. The model’s parameters become sensitive to the statistical traces of philosophical work because philosophical work leaves traces in text. But then there is the inference-side point. At runtime, the prompt is not an external instruction that the model “reads” in the human sense and then decides how to obey. It is part of the sequence being continued. The prompt creates a local context. The model computes hidden representations of that context, uses attention to relate the tokens in that context to one another, and then produces a distribution over possible next tokens. So the prompt changes which learned regularities are relevant. That matters a lot. A prompt like “Explain the Mary argument” places the model in a context resembling countless Q&A, textbook, encyclopedic, and assistant-answer contexts. The model’s instruction tuning also reinforces this: when asked an explanatory question, produce a helpful, balanced, safe, accessible answer. So the model is likely to enter a “generic explanation” regime. It may produce correct philosophy-themed prose, but it is drawing on a region of the learned distribution where the expected continuation is exposition. A prompt that begins halfway through a serious philosophical problem does something different. Suppose it contains: a target phenomenon, two rival candidate accounts, a specific objection to one account, an explanation of why the obvious reply is ad hoc, and a request for a repair that preserves strength without multiplying commitments. That context resembles a different kind of training sequence. It resembles argumentative philosophy in motion. The model’s continuation is now conditioned by local features that call for comparison, repair, cost-accounting, and theoretical choice. It has not become an abductive reasoner. But the continuation-space has been narrowed toward patterns learned from texts where human abductive and dialectical reasoning was expressed. This is why the Section 2 response matters. Floridi says: the model only produces plausible continuations. The technically grounded reply is: yes, but plausibility is conditional on context and learned from a corpus whose structure is not philosophically inert. So the practical question becomes: how do we construct a context in which the plausible continuation is one shaped by philosophical standards? That is the better account of prompting. ## Answer The technical story should be something like this. LLMs are trained to predict continuations. During pretraining, the model sees sequences of tokens and updates its weights so that, in similar contexts, it assigns higher probability to the next token that actually occurred. Over enough data, this produces sensitivity not just to words, but to recurring structures in discourse. A model trained on philosophical material therefore learns more than vocabulary. It learns patterns of philosophical continuation: how objections are introduced, how replies are developed, how distinctions are motivated, how theories are compared, how costs are registered, how a counterexample forces revision. This does not mean the model learns philosophy as a set of rules. It means the model’s probability distribution is shaped by the textual regularities left by philosophical practice. The model does not need to know what an ad hoc repair is in order to become more likely, in the right context, to produce continuations resembling non-ad-hoc repairs. It does not need to understand Liptonian loveliness in order to learn that, after certain kinds of explanatory pressure, good philosophical texts tend to supply unifying rather than merely patching moves. That connects directly to Section 2. Floridi’s point is that LLMs do not perform abduction. They do not internally generate rival hypotheses and select the one that best explains the data. Section 2 grants this. But if the model has been trained on texts in which human beings have publicly performed that abductive work, then the model can learn the distributional traces of that work. The abductive process is not inside the model as a mental act; it is in the corpus as a pattern of written transitions. The model learns those transitions. Prompting matters because the prompt selects which learned transitions are brought to bear. At inference time, the model is not answering from nowhere. The prompt becomes the immediate context for continuation. It changes the hidden state from which the next-token distribution is computed. A weak prompt gives a weak conditioning context. “What is consciousness?” or “Explain the Mary argument” activates patterns associated with general explanation, educational summary, and assistant-style balance. The model has seen a huge amount of text in which such contexts are followed by survey prose. So survey prose is what becomes probable. A stronger prompt changes the local continuation problem. It might specify: - the explanandum, - the candidate view under pressure, - the rival view, - the objection, - the failed obvious reply, - the theoretical cost to avoid, - the virtue being tested. Now the model is not merely continuing a topic. It is continuing a structured philosophical situation. This matters technically because the model’s learned distribution contains many examples of what follows such situations in philosophical prose: a distinction, a concession, a repair, a comparison, a diagnosis of why a rival fails, a clarification of what the explanandum really requires. So the difference is not: ```text bad prompting = vague good prompting = detailed ``` It is: ```text bad prompting = conditioning the model on philosophical subject matter without conditioning it on philosophical pressure good prompting = conditioning the model on a local structure that calls for the kinds of transitions philosophical practice has selected for ``` This also explains why generic LLM philosophy is usually bad. The model has multiple overlapping learned regimes. It has learned academic philosophy, but it has also learned Wikipedia summaries, student essays, blog posts, encyclopedia entries, SEO explainers, Reddit debates, exam answers, and assistant-style “here are both sides” completions. Instruction tuning often strengthens the helpful explainer regime. So when the prompt is generic, the model often defaults to the safest high-probability genre: competent exposition. That is not because the model lacks all philosophical structure. It is because the prompt has not made the philosophically interesting structure locally relevant. Good prompting has to fight that default by constructing a context whose continuation is naturally argumentative rather than expository. It has to put the model in the middle of the kind of sequence where, in the training corpus, philosophical work actually happens. This gives a much more technical way to say how Section 2 informs prompting: If the model is a continuation engine, and if philosophical reasoning is encoded in the corpus as patterns of continuation, then prompting is the method of setting the initial conditions under which those patterns are expressed. The prompter’s role is not just to “ask well.” The prompter constructs a local fragment of inquiry. The model continues that fragment using learned regularities from prior texts. The philosopher then evaluates whether the continuation has the virtues Section 2 discusses: whether it offers a genuine potential explanation, whether it compares live alternatives, whether it avoids ad hoc repair, whether it increases unity or strength, whether it makes the problem more intelligible. This keeps the Floridi concession intact. The model still has no inner abductive selection. But the human-LLM system can exploit a corpus shaped by abductive selection. The model supplies continuations learned from the public residue of philosophical reasoning; the prompt determines which residue is activated; the philosopher supplies assessment and further pressure. So a more precise formulation would be: Worthwhile LLM philosophy becomes possible when the prompt conditions the model on a state of inquiry whose likely continuations are drawn from the learned patterns of serious philosophical reasoning rather than from the learned patterns of generic philosophical exposition. That is the technical link between Section 2 and the question of getting good philosophy out of the model. Raw link: ```text obsidian://open?vault=My%20Obsidian%20Vault&file=Writing%2Fresearch%2Fgenerating-philosophy-text-internal-evaluation%2F2.%20The%20challenge%20from%20abduction ``` *Il punto tecnico è che il prompt imposta le condizioni iniziali della continuazione, e quindi decide quali tracce del lavoro filosofico sedimentato nel corpus diventano operative.* > that was great. now we are getting somewhere. again, just for my own interest raher than anything that i definitely want in the draft is how what you have just said might be connected up to my idea about sellers which i talk about in the hong kong talk (at least i think i do) <details><summary>4 previous messages</summary> > I’ll treat this as exploratory, and I’ll keep it tied to the technical account we just got to rather than pretending this is draft-ready. Since Sellars is an academic source and the vault rules ask me not to discuss author frameworks from memory when the text may be available, I’m quickly checking what source material is actually present before I lean on the quote. > > <details><summary>Explored 1 file, 1 list, ran 1 command</summary> > > <details><summary>Explored 1 file, 1 list</summary> > > - Read `./.codex/skills/source-work/SKILL.md` > - Listed files > > </details> > > - Ran `qmd search "Sellars Philosophy and the Scientific Image hang together broadest possible sense" -n 10` > > </details> > > There is a local Sellars note, so I’m reading that rather than leaning on memory. I’ll distinguish what Sellars says, what your Hong Kong talk did with it, and the speculative connection to the technical prompting/training account. > > <details><summary>Explored 4 files</summary> > > - Read `./Notes/Generality of the learning regime as Sellarsian fit for LLM philosophy.md` > - Read `./Notes/Sellars-LLM generality affinity belongs after the defensive sections.md` > - Read `./Notes/PHILOSOPHY AND THE SCIENTIFIC IMAGE OF MAN by Sellars.md` > - Read `./Notes/Generating Philosophy — Talk Transcript (Lingnan–Genoa–Kobe, 2026-04-23).md` > > </details> </details> ## Analysis Yes, this connects much better than I first saw. The Sellars thought is not just a decorative reason to prefer generalist models over specialist philosophy models. It can be connected to the technical account of training and prompting in a fairly deep way. Let me separate three layers. First, what the Sellars line is doing in your Hong Kong talk. In the transcript, it comes after the distillate/distillation point and after the thought that we need to elicit good philosophy rather than simply ask philosophical questions. You then ask whether specialist philosophy LLMs would be the right direction. The Sellarsian answer you float is: maybe not, because philosophy is not a narrow domain with a limited subject matter. It is concerned with how things in the broadest sense hang together in the broadest possible sense. So a model trained only on philosophy might be less well-suited to philosophy than a broadly trained model, because philosophy itself needs broad cross-domain material. Second, your existing capture note makes a stronger version of this: the affinity is not only breadth of corpus, but “the generalizing character of the learning regime itself.” That is the part that connects to the technical story. Next-token prediction over a massive mixed corpus forces the model to build representations that are portable across wildly different contexts. It has to learn patterns that help it predict not just in philosophy papers, but across science, fiction, law, forum posts, manuals, mathematical exposition, ordinary dialogue, journalism, and so on. The model is pressured into forming abstractions that travel. That looks Sellars-adjacent because Sellarsian philosophy is not mastery of a local subject matter, but the reflective integration of many subject matters. Third, this changes the prompting story. If philosophy is a “hanging together” activity, then good prompting is not only about activating the philosophical subcorpus. That was my previous account, and it is incomplete. The better thought is: good philosophical prompting may activate relations between subcorpora. It brings the philosophical corpus into contact with material from elsewhere: physics, psychology, ordinary phenomenology, literature, legal reasoning, design practice, medicine, whatever is relevant. A purely philosophy-specialist model might know the canonical dialectic, but it may lack the broad representational field across which philosophy often moves. So the connection is something like this. Section 2 says that LLMs learn from written traces of reasoning. A model trained on philosophy learns patterns of objection, reply, explanation, distinction, and comparison. But Sellars gives you a reason to resist thinking that the relevant training base is just “the philosophy corpus.” If philosophy aims to understand how things generally hang together, then the relevant training base includes the many discourses among which philosophy tries to find relations. That matters technically because the model’s learned representations are not just separate lookup tables for different domains. In a transformer, training over heterogeneous text encourages shared internal features and cross-context generalization. The same representational machinery has to serve explanations in physics, agency in fiction, justification in epistemology, responsibility in law, perception in psychology, and so on. I am not claiming we know exactly what any specific model represents internally, but the training pressure is toward portable structure: patterns that can be reused across contexts because they improve prediction across contexts. That is where Sellars becomes more than a slogan. The Sellarsian fit is not merely: ```text philosophy is broad; LLMs are trained on broad data ``` It is more like: ```text philosophy is the reflective integration of many discursive domains; LLMs are trained by finding transferable patterns across many discursive domains; so their technical mode of competence may be unusually well matched to the kind of cross-domain integration philosophy requires. ``` Now connect this back to prompting. On the earlier account, a good prompt conditions the model on a local philosophical pressure: rival views, objections, explanatory burden, cost, repair. That is still right. But the Sellarsian addition is that good philosophical prompting may also condition the model on the right range of extra-philosophical material. It does not merely say “continue this philosophy dialectic.” It says: bring this philosophical problem into contact with the relevant pieces of the manifest and scientific images, or with ordinary practice, or with some other structured discourse. For example, a bad prompt about Mary asks for the familiar dialectic. A better Section 2-style prompt sets up a candidate explanation and rival views. A Sellarsian prompt might go further: it asks how the Mary argument sits between physical information, ordinary colour experience, scientific accounts of perception, and the philosophical demand to locate phenomenal knowledge within a broader picture. The point is not just to answer the argument, but to make domains hang together. This also reframes the specialist-model question. A specialist philosophy model might be better at reproducing professional dialectical moves. It might know the canonical objections and replies. But if philosophy is broad integration, specialisation could be a loss. It might narrow the model’s access to the very cross-domain patterns that philosophy needs. The general model may be philosophically useful not despite being trained on everything, but because being trained on everything gives it material for Sellarsian integration. There is a nice contrast here: Floridi-style objection: the model only tracks statistical continuation. Section 2 response: yes, but statistical continuation over philosophically shaped text can reproduce public patterns of reasoning. Sellarsian extension: and because philosophy itself concerns how many domains hang together, the relevant “philosophically shaped” material is not confined to philosophy texts. A general corpus may be philosophically productive precisely because it contains the many domains philosophy tries to integrate. So the technical account becomes broader. In pretraining, the model is not only absorbing local argumentative forms from philosophy. It is also absorbing cross-domain correspondences: how scientific explanation talks, how everyday experience is described, how law handles responsibility, how fiction renders agency, how psychology explains perception, how ordinary speakers use moral concepts. A philosophical prompt can then make some of these representational resources bear on a problem. That is a very Sellarsian picture of prompting: not “ask the philosophy module,” but “stage a context in which heterogeneous domains can be brought into reflective relation.” This also explains why the prompt matters even more on the Sellarsian picture. If the model contains many domains, the prompt has to select and coordinate them. Generic prompts let the model fall into generic exposition. Narrow philosophy prompts may activate only the professional dialectic. Strong Sellarsian prompts specify which domains need to be brought into relation and what kind of relation is being sought: reconciliation, reduction, tension, explanation, translation, containment, revision. This gives a different taxonomy of good prompting, for your own thinking: - Local dialectical prompting: activates objection-reply structures within philosophy. - Explanatory prompting: asks for candidate explanations and costs, tied to Section 2. - Sellarsian integrative prompting: asks the model to relate discourses or images, to show how a philosophical claim fits with scientific, ordinary, aesthetic, legal, or phenomenological material. - Iterative integrative prompting: tests whether the proposed integration survives pressure from each domain in turn. That last one feels important. Good philosophy from LLMs may not be a single prompt that says “connect X and Y.” It may involve cycling through domains: now test the view from ordinary experience; now from cognitive science; now from the history of the concept; now from the rival philosophical account. That is the practical form of “how things hang together.” ## Answer I think your Sellars idea connects to the technical training/prompting account in two ways. The first connection is about the corpus. Section 2 says that the model learns the written traces of philosophical reasoning from the philosophical corpus: objection, reply, distinction, explanatory comparison, cost, repair. But Sellars suggests that the philosophically relevant corpus is wider than philosophy narrowly construed. If philosophy is about how things in the broadest sense hang together, then the material philosophy works on includes science, common sense, literature, law, psychology, history, aesthetic practice, ordinary descriptions of experience, and so on. A general-purpose LLM has been trained across exactly this kind of mixed discursive field. So the general model may be philosophically useful not because it has a special philosophy module, but because its training has forced it to form portable patterns across many domains. That is the technical Sellarsian affinity: philosophy seeks broad integration; broad next-token training rewards representations that generalize across contexts. The second connection is about prompting. On the Section 2 account, good prompting conditions the model so that learned philosophical transitions become relevant. The Sellarsian addition is that good prompting may also condition the model so that multiple domains become jointly relevant. It asks the model not merely to continue a philosophical dialectic, but to bring different discourses into relation. So there are two kinds of prompting: ```text Narrow philosophical prompting: Given this objection to this view, repair the view without ad hoc cost. Sellarsian prompting: Given this philosophical problem, show how it sits between ordinary experience, scientific explanation, and the relevant philosophical theory; identify where the pictures conflict and what kind of integration would reduce the tension. ``` The second kind is where general-purpose models may have a special advantage over specialist models. A specialist philosophy model might be good at professional moves inside the literature. But a general model has been trained on the broader field of articulated human understanding. If Sellars is right about philosophy, that breadth is not noise. It is part of philosophy’s subject matter. This also changes how to think about bad prompting. A bad prompt is not only one that gives philosophical subject matter without philosophical structure. It may also be one that artificially narrows the model to the internal dialectic of philosophy when the problem actually requires cross-domain integration. It asks for “the argument about Mary” rather than asking how the Mary case relates physical information, phenomenal description, ordinary learning, and philosophical explanation. So the Sellarsian version of the prompting idea is: Good prompting gets worthwhile philosophy from a general model by staging a local problem in which different parts of the model’s broad training have to be brought into reflective relation. That sits nicely with your Hong Kong thought about resisting specialist philosophy models. The reason to resist specialisation is not just that philosophy is broad in subject matter. It is that the technical competence produced by general training may be closer to philosophical competence than a narrow philosophy-trained competence would be. General training gives the model access to the many domains philosophy tries to integrate; prompting is how the philosopher makes those domains bear on a particular problem. Raw links: ```text obsidian://open?vault=My%20Obsidian%20Vault&file=Notes%2FGenerating%20Philosophy%20%E2%80%94%20Talk%20Transcript%20%28Lingnan%E2%80%93Genoa%E2%80%93Kobe%2C%202026-04-23%29 ``` ```text obsidian://open?vault=My%20Obsidian%20Vault&file=Notes%2FGenerality%20of%20the%20learning%20regime%20as%20Sellarsian%20fit%20for%20LLM%20philosophy ``` *La connessione sellarsiana non è solo che il modello conosce molti argomenti, ma che la sua competenza nasce da un addestramento alla portabilità tra molti modi di descrivere il mondo.* > so you think the best way to connect sellers is as a rival way of prompting, compared to what you just talked about based on my section 2 and the technical details? ## Analysis Not exactly. I would not treat Sellarsian prompting as a rival to the Section 2-based prompting account. I think it is better understood as an extension or second layer. The Section 2-based account answers this question: How can a model that does not perform abduction internally produce a text that has the structure of a candidate explanation or philosophical move? Its answer is: because the model has learned continuation-patterns from texts already shaped by philosophical reasoning, and the prompt can condition the model so that those patterns become operative. The Sellarsian account answers a different question: Why might a broadly trained general model be especially apt for philosophy, rather than needing to be narrowed into a specialist philosophy model? Its answer is: because philosophy itself is not a narrow domain. It is an integrative activity concerned with how many kinds of thing hang together. A broad model’s training over many discourses may be an advantage precisely because it gives the model cross-domain representational resources. So I would not say: ```text Section 2 prompting = one method Sellarsian prompting = rival method ``` I would say: ```text Section 2 gives the local mechanism. Sellars gives the global rationale for why general models, rather than specialist models, may be useful for that mechanism in philosophy. ``` Section 2 explains how prompting works at the level of a local philosophical task: the prompt conditions a continuation so that the model draws on learned patterns of objection, explanation, repair, and comparison. Sellars explains why the relevant learned material should not be restricted to philosophy narrowly construed: philosophy often works by bringing many discourses into relation, so the broad training regime itself may be philosophically relevant. ## Answer No, I do not think Sellars is best treated as a rival way of prompting. I think the better relation is: - Section 2 gives you a theory of local philosophical prompting. - Sellars gives you a theory of why general-purpose LLMs may be apt for philosophy at all. The Section 2 account says: good prompts create a local context in which the model’s learned patterns of philosophical reasoning become relevant. That is about objection, explanation, candidate theories, non-ad-hoc repairs, and assessable philosophical moves. The Sellars account says: philosophy is not just a closed professional game played inside the philosophy literature. It is the attempt to understand how things in the broadest sense hang together. So a model trained across science, literature, law, everyday speech, psychology, history, and philosophy may have an advantage over a model trained only on philosophy. Its breadth is not incidental; it matches the breadth of philosophy’s subject matter. So Sellars does not replace the Section 2 prompting picture. He widens it. A good prompt, on the Section 2 picture, asks the model to produce an assessable philosophical move. A good prompt, with the Sellarsian layer added, asks the model to produce an assessable philosophical move that draws the right domains into relation. For example: ```text Section 2 style: Given this objection to physicalism, formulate a non-ad-hoc repair and compare it with the rival dualist explanation. Sellarsian extension: Given this objection to physicalism, formulate a repair that makes the philosophical claim hang together with ordinary colour experience, neuroscience, and the methodological role of thought experiments. ``` The first asks for a local dialectical move. The second asks for a local dialectical move with cross-domain integration built in. That seems the strongest connection. Sellars is not a competing prompting technique. Sellars explains why broad, cross-domain prompting may be philosophically important, and why a general model might be better suited to that task than a narrowly specialised philosophy model. *Sellars non sostituisce il modello locale del prompt, ma amplia ciò che deve essere messo in relazione dentro il problema filosofico.*