spex-reflect

To improve AI memory after feature development, analyze completed cycles to identify missing context and generate actionable memory updates. This process pinpoints information the AI lacked and proposes new policies or domain knowledge to prevent future mistakes.

Feature reflection in knowledge management is the process of analyzing completed development cycles to flag confusing or outdated context. It identifies gaps in AI knowledge and proposes concrete deprecations or updates to optimize future AI performance.

You identify missing context by analyzing conversation transcripts, plan artifacts, and trace records from completed development cycles. This analysis highlights outdated information or missing requirements that confused the AI, enabling targeted knowledge management updates.

Yes, generating comprehensive AI memory updates requires access to plan artifacts, decision records, requirement records, trace records, metrics reports, and conversation transcripts. These inputs are necessary to accurately identify missing context and propose actionable policies.

Yes, post-development reflection analyzes completed feature cycles to propose actionable memory updates for AI systems. It generates concrete suggestions for new policies, domain knowledge additions, and context deprecations based on identified knowledge gaps.

You should analyze completed feature development cycles for context refinement immediately after deployment to capture accurate insights. This post-development reflection identifies missing or confusing context and updates AI memory to ensure future features are built more smoothly.