karpathy-guidelines

To prevent LLM coding errors, apply behavioral guidelines that enforce a simplicity-first mindset, require explicit assumption surfacing, and mandate surgical editing to avoid speculative abstractions and unnecessary refactoring.

The best way to stop AI from over-engineering during refactoring is to use a surgical change protocol that ensures precise edits, minimizes regression risks, and restricts modifications strictly to requested functionality.

You can enforce simplicity and precision for LLM code review by applying behavioral guardrails that check for unverified assumptions, unnecessary complexity, and deviations from explicit goal-driven execution patterns.

An LLM adds speculative abstractions because it lacks inherent behavioral constraints. Applying goal-driven execution guidelines forces the model to surface explicit assumptions and implement only the requested functionality.

Yes, you can use these coding heuristics for any software development task involving code generation, review, and refactoring where simplicity and precision are critical to maintaining minimal, maintainable software.