self-correction-loop

To stop Claude from repeating coding mistakes, you can use a self-correction loop that captures your feedback, extracts generalized rules, and stores them permanently in a MEMORY.md file. This ensures the AI applies your corrections automatically in future sessions.

A self-correction mechanism converts specific feedback into reusable rules by detecting when you reject an output, generalizing the specific fix into a category-level rule, and deduplicating it against existing memory entries before storing it in MEMORY.md.

Yes, you can persist developer productivity rules across different Claude sessions by storing generalized corrections in MEMORY.md. The system reviews this file at the start of each session to apply existing rules immediately as a baseline.

The best way to deduplicate memory entries when saving AI corrections is to scan existing memory for overlaps and update related rules instead of adding near-duplicates. This maintains a clean knowledge base and prevents redundant guidance.

No, this memory management approach requires no specific dependencies or components. It operates independently by using deterministic storage formatting to write generalized rules directly into your project's MEMORY.md file.

You should not use a self-correction loop for prompt improvement if your session involves one-off, highly specific tasks that will not recur. The system is designed to generalize corrections into permanent rules, which adds unnecessary overhead for isolated interactions.