{"id":5953,"date":"2026-08-17T09:27:43","date_gmt":"2026-08-17T09:27:43","guid":{"rendered":"https:\/\/www.aviator.co\/blog\/?p=5953"},"modified":"2026-08-17T09:27:45","modified_gmt":"2026-08-17T09:27:45","slug":"ai-code-review-best-practices","status":"publish","type":"post","link":"https:\/\/www.aviator.co\/blog\/ai-code-review-best-practices\/","title":{"rendered":"AI Code Review Best Practices"},"content":{"rendered":"\n

A new subdomain of traditional software development has emerged, and it revolves around machines reviewing machines. AI now writes most of the code, and it\u2019s physically impossible for a human to sit down and go through it all while remaining laser-focused.<\/p>\n\n\n\n

That\u2019s why this article will discuss the deepening trench between how fast AI can write useful (or less useful) code and how fast you can review it, especially after the lunch break, when your attention span is hanging on by a thread. We\u2019ll cover what the best practices for using AI to review AI-generated code are.<\/p>\n\n\n\n

Yours truly, a human.<\/p>\n\n\n\n

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TL;DR<\/strong> When reviewing AI-generated code, the best approach is to focus on the intent before AI has generated a ginormous diff no one wants to read. Keep changes small, and layer your tools instead of putting all your trust in one reviewer. Verify behavior by running the code, and encode your recurring rules once so you don\u2019t have to repeat yourself. And remember, a human should always make the final call.<\/p>\n<\/blockquote>\n\n\n\n

Reviewing AI Code As a Separate Role?<\/h2>\n\n\n\n

At this point, writing code doesn’t take nearly as much time or brainpower as it used to. Reviewing it is a different story. AI can write tons of code in a short amount of time, but unsurprisingly, humans just can\u2019t keep up.<\/p>\n\n\n\n

Another thing to keep in mind is that AI code actually gets worse the more you iterate. A study accepted at IEEE ISTAS 2025<\/a> discovered a 37.6%<\/strong> rise in critical vulnerabilities after just five rounds of asking a model to “improve” its own code.<\/p>\n\n\n\n

This is how the role of a reviewer has changed. They no longer check if code compiles and whether it\u2019s clean. They now have to verify and prove intent. Does the code solve the problem that the key stakeholders intended it to, both from a business and a logical perspective?<\/p>\n\n\n\n

Best Practices<\/h2>\n\n\n\n

So, how can you review AI code without rubber-stamping it or reading 1,800 lines one by one? Here are six practices that have worked well for us (as of July 2026, at least; good luck if you\u2019re reading this in the age of AGI).<\/p>\n\n\n\n

1. Review the Intent Before Reviewing the Diff<\/h3>\n\n\n\n

Instead of trying to figure out what a change was for<\/em> by reading the code, you need to agree on the goal first. Start with a short scope + a few concrete acceptance criteria, such as \u201creject a page size above 100 with a 400<\/code>\u201d.<\/p>\n\n\n\n

A human approves that intent first. The review then becomes about checking whether the implementation meets that criterion, rather than trying to work out whether all of it is correct.<\/p>\n\n\n\n

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\u26a0\ufe0fTrade-off<\/strong>: Writing criteria upfront can feel slower, and for a one-line typo fix, it\u2019s probably overkill. But the goal isn’t to write more docs, it’s to make capturing intent a habit for changes that affect behavior, security, or money. With Verify<\/a>, that intent (and the acceptance criteria) can be captured from the implementation and the prompt, so it adds little to no extra work.<\/p>\n<\/blockquote>\n\n\n\n

2. Keep Changes Small, and Make the Agent Do It<\/h3>\n\n\n\n

Small PRs get real reviews, whereas large PRs get an LGTM<\/code> and a prayer. The underlying issue here is the span of human attention, there\u2019s only so much of it to go around.<\/p>\n\n\n\n

As a solid structural fix, you can ask your agent to ship the change as a stack of small, interdependent PRs rather than one giant blob.<\/p>\n\n\n\n

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\u26a0\ufe0fTrade-off<\/strong>: Stacking adds some overhead. Plus, not every change breaks down neatly. A coherent refactor sometimes belongs in one PR. Again, use your judgement.<\/p>\n\n\n\n

To break large changes into small, reviewable ones, use the Stacked PRs CLI<\/a>.<\/p>\n<\/blockquote>\n\n\n\n

3. Don\u2019t Use One AI Reviewer, Layer Your Tools Instead<\/h3>\n\n\n\n

No single reviewer, human or a model, can catch everything. A proper review pipeline uses multiple layers:<\/p>\n\n\n\n