ai-factory.improve

Refining an existing project plan involves re-analyzing it against the codebase to identify gaps, missing tasks, and incorrect dependencies. This process applies plan context and patches to generate prioritized improvements while preserving completed work.

To fix incorrect task dependencies in an implementation plan, use dependency reconciliation to detect missing or wrong links and propose fixes. This ensures plan integrity by updating task content and dependencies with clear justifications.

AI project plan refinement is the process of re-evaluating an active plan against codebase changes, patches, and task lists. You need it when your plan has gaps, incorrect dependencies, or requires quality improvements after initial implementation.

Plan refinement preserves completed work when updating task descriptions and plan files. It suggests concrete updates to dependencies and task content while providing clear justifications for each change to maintain overall plan integrity.

You can use project context like description files and past corrections to prevent recurring plan errors during refinement. Context-aware guidance leverages this history to ensure updates prevent the recurrence of previous mistakes.