# Scaffolding gradient as competence metric Philosophical competence in LLMs can be measured by how much prompting/scaffolding is needed before the model reliably produces good work. This turns "LLM philosophical ability" into an empirical question about constraint engineering. ## The Research Question > To what degree can current LLMs be prompted into producing "Good Philosophy," and what does the required "Scaffolding" reveal about their internalisation of the Space of Reasons? ## The Metric We measure "Philosophical Competence" by the **Scaffolding Gradient**: | Level | Description | Scaffolding Required | |-------|-------------|---------------------| | **Level 1** | Generic | Massive hand-holding (checklists, logic-flags) | | **Level 2** | Dialectical | Responds to [[Resolution control and LLM philosophy|Resolution Control]] ("be more precise") | | **Level 3** | Autonomous | Produces a [[Move 37 and philosophical novelty|Move 37]] with a single "[[The obvious move prompting technique|Obvious Move]]" nudge | ## Why This Matters This framing: - Acknowledges that AI's "Internalisation" is a **matter of degree** - Provides a testable-looking thesis without pretending to solve the metaphysics of understanding - Treats prompting as **constraint engineering** rather than content engineering - Makes "LLM philosophical ability" about how much constraint engineering the user must supply ## The Goal If the model can satisfy dialectical and logical constraints with less external scaffolding, it has more deeply internalised the norms of philosophical continuation. A "better" philosophy-capable LLM would be one that requires less "Resolution Management" to stay precise. Source: [[Web Clippings/chat with ChatGPT about Floridi, LLM reasoning, Move 37|ChatGPT conversation on Floridi et al.]]