kimchi:halmoni

AI self-improvement for planning works by analyzing past execution outcomes and user feedback to propose, validate, and apply incremental enhancements to AI skills, validators, and personas. This iterative refinement ensures planning logic becomes more accurate over time.

You make AI planning logic learn from past execution outcomes by running a self-improvement system that analyzes execution logs to identify where the AI struggled. It then suggests specific adjustments to planning skills or personas to prevent similar issues in the future.

Yes, you can version and log incremental enhancements applied to AI validators, skills, and personas. The system automatically updates skills and logs changes, ensuring a traceable improvement history for all applied modifications.

The best way to use user feedback for AI self-improvement is to feed it directly into a validation system that observes both the feedback and execution results. The system then proposes and applies validated enhancements to refine execution logic.

Yes, you need execution logs or direct user feedback to propose and validate adjustments to AI personas. The system analyzes these inputs to identify specific areas where the AI struggled during a planning cycle to suggest targeted improvements.

AI planning produces suboptimal plans when execution logic struggles with specific task constraints. Analyzing execution logs from the failed cycle allows a self-improvement system to identify these struggle points and apply targeted skill adjustments to prevent future issues.