AI Code Review Best Practices

A study accepted at IEEE ISTAS 2025 found a 37.6% rise in critical vulnerabilities after five rounds of AI self-improvement, highlighting the need for human review of AI-generated code. Best practices include reviewing intent before the diff, keeping changes small, layering tools, verifying behavior by running code, and encoding recurring rules, with a human making the final call.

AI Code Review Best Practices 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’s physically impossible for a human to sit down and go through it all while remaining laser-focused. That’s 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’ll cover what the best practices for using AI to review AI-generated code are. Yours truly, a human. TL;DRWhen 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’t have to repeat yourself. And remember, a human should always make the final call. Reviewing AI Code As a Separate Role? 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’t keep up. Another thing to keep in mind is that AI code actually gets worse the more you iterate. A study accepted at IEEE ISTAS 2025 https://arxiv.org/abs/2506.11022 discovered a 37.6% rise in critical vulnerabilities after just five rounds of asking a model to “improve” its own code. This is how the role of a reviewer has changed. They no longer check if code compiles and whether it’s 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? Best Practices 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’re reading this in the age of AGI . 1. Review the Intent Before Reviewing the Diff Instead of trying to figure out what a change was for by reading the code, you need to agree on the goal first. Start with a short scope + a few concrete acceptance criteria, such as “reject a page size above 100 with a 400 ”. 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. ⚠️ Trade-off: Writing criteria upfront can feel slower, and for a one-line typo fix, it’s 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 , that intent and the acceptance criteria can be captured from the implementation and the prompt, so it adds little to no extra work. 2. Keep Changes Small, and Make the Agent Do It Small PRs get real reviews, whereas large PRs get an LGTM and a prayer. The underlying issue here is the span of human attention, there’s only so much of it to go around. 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. ⚠️ Trade-off: Stacking adds some overhead. Plus, not every change breaks down neatly. A coherent refactor sometimes belongs in one PR. Again, use your judgement.To break large changes into small, reviewable ones, use the Stacked PRs CLI . 3. Don’t Use One AI Reviewer, Layer Your Tools Instead No single reviewer, human or a model, can catch everything. A proper review pipeline uses multiple layers: - A linter or static application security testing SAST tool for deterministic, known-bad patterns fast, reproducible, but intent-blind - An AI reviewer for a quick second opinion on logic and readability useful, but non-deterministic since you’ll get different comments if you run it twice - A set of human brains for the final call The trick is to keep the feedback-to-noise ratio well-balanced. A reviewer that leaves a dozen, low-value comments aka nitpicks per PR starts to get ignored pretty quickly. Same goes for AI leaving a kilometer long PR description that gets you nowhere. ⚠️ Trade-off: More layers also means larger cost and more configuration. Plus, if you stack two noisy tools, it will just leave you with more alert fatigue rather than more safety. 4. Don’t Infer Behavior, Verify It You can’t really tell just by reading a diff whether a function returns the right result under specific conditions. Your seniority might help, but you can’t rely on it fully. Behavior should be observable, not something you have to deduce from a diff read. So, run the function, see the preview deployment, and watch the fireworks. ⚠️ Warning:Do not treat AI-written tests as independent proof. If the same agent has misread the ticket, it will happily write passing tests for its own wrong code. 5. Encode Recurring Rules Every team has the same five comments it leaves on every review. Writing them by hand forever is, first, boring and time-consuming, and second, something seniors don’t really want to spend time on. Encode them once as reusable rules. Some tools call these invariants https://docs.aviator.co/verify/concepts/invariants , others have a STANDARDS.md … You get the gist. Either way, there will be no need to nitpick again.⚠️ Warning:Keep in mind that a rule written for last year’s architecture may start flagging false positives as things change. Rule sets tend to rot. 6. Keep a Human on Payroll, and Keep an Audit Trail A human should still approve the intent and resolve failures. Accountability can’t be automated, and you still need someone in charge of the decision to ship. Also, if you work anywhere near compliance SOC 2, ISO 27001, and other word-number mixes , you need to keep an immutable record of: - Who approved what - When they approved it - Which criteria it was approved against Keeping actors and responsibilities separate is not plain bureaucracy. Auditors really need it. ⚠️ Trade-off: Process overhead, yes. But it’s still cheaper than reconstructing a paper trail the week before an audit, or explaining a breach after one. ｀・ω・´ Where the Manual Approach Breaks Now imagine doing all six of these by hand, on every PR, forever. Well that’s basically a second job. The intent-first and run-it-for-real practices are the ones that tend to stick around. A managed option, Aviator Verify https://verify.aviator.co/ , operationalizes exactly those kinds of practices that are a pain to do by hand. You capture intent through the Aviator Model Context Protocol MCP before the PR, and Verify checks each approved acceptance criterion against the running code: - Structural facts go to a code scan of the abstract syntax tree AST . - Behavioral ones get exercised on a preview deployment. - Recurring team rules match as invariants. - A human reviews the behavior, the intent, and the decision being made instead of staring at a diff. Every result is packed with evidence, and you get an audit trail that should make any auditor very happy, indeed. 🙂 However, before we get carried away, we need to keep it straight about the limits: - Verify does not replace your linters, scanners, or human reviewers. - You still approve the intent and resolve the failures. - Verification and review cover different blind spots, so they need to be run together. Tool or no tool, the verdict stays the same. You need to stop asking “Does this code look okay?” and start asking “Does this do what we’ve agreed to build?” Love it or hate it, AI-written code is already in your repo. Reviewing it properly is the job now. You can find the setup at aviator.co/verify https://www.aviator.co/verify and docs.aviator.co/verify https://docs.aviator.co/verify . No credit card required. Frequently Asked Questions FAQ Is AI code review worth it? Very much so, particularly when it works without forcing developers into a separate UI and juggling multiple tools. Can AI replace human code review? No. AI reviewers catch bugs, security issues, and style problems, but they can’t be accountable for business context or the decision to ship. They simply make human reviews sustainable at higher PR volumes. What is the difference between AI code review and AI code verification? Review reads the diff and infers intent, producing different comments on each run. Verification checks code against acceptance criteria approved beforehand, so the result is reproducible. They cover different blind spots and work well together.