Ask the Model to Introspect

~24 min · introspection, drift-audit, evidence

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Confidence is not signal

Do not ask 'do you understand?' The model will say yes. Do ask: list live rules, list things you must not do, name the source of each fact, and identify what you think might be missing. Evidence-based introspection catches drift that confidence prompts hide.

Schedule introspection around risk

Run a drift audit before compaction, after a long interruption, before commit, before publish, and after switching tasks. These are the moments where unverified assumptions cost the most. Reactive introspection — after something already broke — has lower yield.

Frame the audit to admit gaps

The audit prompt should explicitly invite the model to say 'missing.' If the prompt is shaped like a quiz the model wants to pass, it will reconstruct from priors instead of admitting blanks. Build a prompt that rewards honest gaps.

Code

Drift audit prompt·text

Drift audit:
Using only current context (no general knowledge), list:
  1. Live rules I must obey, with source.
  2. Actions I must not take, with source.
  3. Sources I have verified this turn (paths/IDs).
  4. The next safe action.
  5. Anything I think might be missing — say 'missing' rather than reconstruct.
Then wait for human confirmation before continuing.

When to run it·yaml

introspection_triggers:
  - before_compaction
  - after_long_interruption
  - before_commit
  - before_push_or_deploy
  - on_topic_switch
  - when_human_corrects_authority_rule
  - first_action_of_a_resumed_session

External links

OpenAI compaction guide

Exercise

Run the drift audit prompt on a current AI session or simulate the output for one. Note where the model confidently reconstructs vs. honestly admits 'missing.'

Hint

The important part is admitting missing context, not passing the quiz.

Progress

Progress is local-only — sign in to sync across devices.

← ⚡ Caching and Stable PrefixesQuiz · 5 questions Next →The Re-Prompt Spiral

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