Source File

  • AI 探索/如何提升 AI 代码质量.md

Summary

Practical checklist for better outcomes with AI-generated code: stronger docs and instructions, mandatory human verification, a staged iteration mental model (first/second/third attempt), context management via tooling, marking human-edited regions, and treating the model as a non-learning junior.

Key Points

  • Docs and specs: Project files (e.g. Claude.md) with architecture, patterns, pitfalls; “document everything” as leverage.
  • Verification: Extra care for state, performance, and security; three-step review (AI pre-review, developer on architecture/business, normal team review).
  • Iteration: Expect rough first output, improving understanding by the second pass, usable baseline by the third; continuous correction.
  • Context: Split problems; connect AI to tickets, docs, DB sandboxes, repo/PR history.
  • Hygiene: Mark human edits so the model does not misattribute code.
  • Mindset: Detach ego from “my code”; focus on the problem solved.

Notes

Aligns with external “95% garbage first attempt” narrative cited elsewhere in the author’s reading list.