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# 6 Best Practices for Human Reviewers: How to Review AI-Generated Code

**[Aviator Blog](https://daily.dev/sources/aviator-blog)** · 5 min read · 0 upvotes · 0 comments

## Summary

Reviewing AI-generated pull requests requires a different mindset than reviewing human code, because AI bugs look confident and polished rather than sloppy. Six practices are recommended: have the agent explain its reasoning before you read the diff, learn to spot AI's tells (invented APIs, plausible-but-wrong logic), scrutinize new dependencies and lockfile changes, watch for the 'looks done' trap of stubbed functions and quietly dropped requirements, treat AI-generated tests with skepticism since they often pass without asserting much, and triage review effort by blast radius (auth, payments, data handling get priority over cosmetic changes). A FAQ section reinforces that reviewers should check PR descriptions and dependencies first, not dive straight into logic, and that not every line needs to be read if attention is properly triaged. The piece also promotes a tool, Aviator Verify, for mechanically checking behavioral and structural criteria against running code.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.aviator.co/blog/how-to-review-ai-generated-pull-requests-at-scale-best-practices-for-2026>

## Questions this post answers

### What should I check first when reviewing an AI-generated pull request?

Start with the PR description and dependency diff rather than the logic itself. Require the agent to explain its reasoning and assumptions, and explicitly state what it did not do, before reading the diff. Checking imports and lockfile changes for new, invented, or unnecessarily heavy dependencies is easier to catch there than buried inside hundreds of lines of implementation code.

_Reviewers standardizing an AI pull request checklist can find more workflow patterns like this on daily.dev._

### Are AI-generated tests reliable enough to trust in a code review?

No, AI-generated tests should be read with the same skepticism as the code itself, especially when the same model wrote both. They frequently assert too little, mock away the actual logic being tested, or validate the wrong behavior entirely, because the goal of the generating model is to sound confident and correct rather than to genuinely verify behavior.

_Anyone deciding how much to trust AI-written tests can dig into more testing practices on daily.dev._

### Do I need to read every line of an AI-generated pull request?

No, reading every line at AI-generated volume is a recipe for letting real problems slip through. Instead, triage by blast radius: give full attention to auth, payments, data handling, concurrency, and anything user-facing and hard to undo, while skimming lower-risk changes like copy tweaks, log lines, or icon swaps.

_Developers managing growing AI code review workloads can track more triage strategies on daily.dev._

## Similar posts on daily.dev

- [AI Code Review Best Practices](https://daily.dev/posts/ai-code-review-best-practices-ii7hbaa3j) · Aviator Blog · 15 upvotes · 1 comments
- [How to review AI generated PRs](https://daily.dev/posts/how-to-review-ai-generated-prs-50unq3881) · thoughbot · 1 upvotes · 0 comments
- [How long before we stop reading the code?](https://daily.dev/posts/how-long-before-we-stop-reading-the-code--omchyubsd) · The New Stack · 1 upvotes · 0 comments
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---

Tags: [#testing](https://daily.dev/tags/testing), [#ai-coding](https://daily.dev/tags/ai-coding), [#code-review](https://daily.dev/tags/code-review), [#vibe-coding](https://daily.dev/tags/vibe-coding)

[View this post on daily.dev](https://daily.dev/posts/6-best-practices-for-human-reviewers-how-to-review-ai-generated-code-xuylblot0)

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