dotnet-mcaf-human-review-planning

To plan a human review strategy for large code drops, you need to identify the target area, map user and system flows, and produce a prioritized file list. This yields a structured workflow with risk-focused outputs and artifact-ready deliverables.

The best way to focus code review on high-risk areas is through risk-focused planning that identifies entry points, persistence, and cross-boundary interactions. This approach ensures safety and alignment with the software architecture.

A review plan artifact helps with hand-off by saving a durable HUMAN_REVIEW_PLAN.md file. This structured artifact provides a step-wise execution plan and prioritized file sets for reviewers to follow.

Yes, you can use this workflow planning approach for scalable AI code reviews. It is specifically structured for large code drops, generating a prioritized file set to ensure review efforts scale safely.

You need a structured workflow for human review when dealing with large AI-generated code drops. It helps focus reviewer attention on high-risk areas and delivers actionable, artifact-ready deliverables for safe integration.

Manual code review for large AI code drops often lacks durable, high-signal planning, causing attention drift from high-risk areas. Without a structured workflow and artifact hand-off, identifying cross-boundary interactions and persistence becomes inconsistent.