How to Choose and Use AI Code Review Tools: A Guide to Automating Your PR Process

A three-person team on a Friday afternoon, with a PR that's been sitting there for twelve hours without anyone looking at it - this scenario is all too familiar. The lead is busy putting out fires, the other two team members are stuck reviewing each other's code, and the code just sits there, going stale. By the time someone finally gets around to looking at the PR on Monday, the changes have piled up to the point where it's almost impossible to review, and everyone tacitly agrees to approve it without really looking, leaving the problems for their future selves to deal with.

This is exactly why AI code review has been rapidly adopted over the past six months: it doesn't get tired, it won't give up on a large PR, and it can run a preliminary review at the earliest opportunity. However, it's not a panacea. In this article, I'll cover the what, why, and how of AI code review, all in one go.

Why this matters now

There's a chain reaction that many teams overlook: AI coding agents have made writing code faster, resulting in more and larger PRs. The tools we discussed in our 2026 AI Coding Agents Landscape have enabled a single person to open twice as many PRs per day - but the number of reviewers hasn't doubled. The production side has accelerated, but the review side is still stuck with the same few people, creating a bottleneck that's shifted from "can't write enough code" to "can't review enough code".

AI code review fills this gap. It runs automatically when a PR is opened, catching obvious bugs, missing error handling, inconsistent naming, and potential security issues, clearing out the "low-hanging fruit" that doesn't require human judgment. Human reviewers can then focus on the parts that truly require judgment: whether the design is reasonable, if there's a simpler way to do it, and if it conforms to team conventions.

For Taiwanese teams, the value lies in "making review no longer a bottleneck". Many of our teams don't have dedicated reviewers, and code review falls to senior engineers who have to squeeze it into their schedules. By having AI run a preliminary review, we can save senior engineers the time spent reviewing low-level issues.

Main tools and differences

AI code review tools have proliferated over the past six months, and I've categorized them by "how they integrate into your workflow":

• cubic: Focuses on PR-stage AI review, automatically analyzing and commenting on your code as soon as you open a PR. Its positioning is clear - it doesn't try to write code for you, only reviews it, complementing production tools like Cursor.
• CodeRabbit: Integrates with GitHub and GitLab, automatically reviewing code line-by-line and providing a summary when you submit a PR. It's been widely discussed in team collaboration scenarios.
• Greptile: Emphasizes understanding the entire codebase, providing context across files during review. It's suitable for large, complex projects.
• Qodo: In addition to review, it also generates tests, combining "review" and "test supplementation" into one.
• Graphite: Originally a collaboration tool for handling stacked PRs, it also integrates AI review capabilities, suitable for teams with high PR traffic.

My advice remains the same as in my previous article: don't ask which one is the strongest, ask what your pain point is. If your pain point is "nobody's reviewing PRs", choose a tool that can automatically run and provide clear summaries; if your pain point is "reviewers can't catch cross-file issues in large projects", choose a tool that emphasizes codebase understanding; if your pain point is "nobody's writing tests", look for a tool that combines review and test generation.

How to use it (steps to integrate into your workflow)

Just installing a tool is just the beginning - using it effectively makes all the difference. Here's my approach:

1. Integrate it into your PR workflow, set it to trigger automatically: Let it run automatically when a PR is opened, don't rely on humans to remember to trigger it manually, or it will definitely be forgotten.
2. First week: just observe, don't enforce: When first introducing the tool, treat AI's suggestions as references, don't set it to "must pass AI review to merge". Observe whether its suggestions are accurate, and whether it's too noisy.
3. Adjust its strictness and scope: Most tools allow you to set rules, turn off items that are always complaining but aren't important to the team, and leave only the truly valuable ones. If you don't do this step, AI review will quickly become noise that everyone ignores.
4. Clearly define the division of labor between humans and AI: Let AI handle bug-catching, error handling, style consistency, and other "standard answer" issues; leave design reasonableness, simplicity, and team convention adherence to humans. The team needs to have a consensus: AI approval is not a substitute for human review.
5. Regularly review false positives: Every so often, inspect its common false positives, and continue to adjust the rules. Treat it like a new reviewer that needs training, rather than installing and forgetting.

Common pitfalls and suggestions

• Noise is the number one killer: AI review is most likely to fail when it "talks too much". If a PR has twenty irrelevant comments, everyone will start ignoring all of them, including the truly important ones. It's better to adjust the strictness to be a little tighter, with fewer but more accurate suggestions.
• Don't let it become a rubber stamp: Some teams see AI approval and directly merge, which is dangerous. AI will miss things, especially when it comes to business logic and requirement understanding, which it can't see.
• Confirm privacy first: Where will your code be sent for analysis? For industries sensitive to code (finance, medicine), confirm data processing methods before introducing the tool, and choose self-hosted solutions when necessary.
• It doesn't understand your 'why': AI can see what the code looks like, but not the business considerations behind it. It may say "this can be simplified", but that complexity might be intentional for a specific edge case. Humans need to retain veto power.

TheAI Academy's perspective

My stance on AI code review is clear: it's meant to "amplify reviewers", not "replace reviewers". The best state is when AI clears out 90% of low-level issues, allowing your senior engineers to focus their precious attention on the 10% that truly requires human judgment.

Comment: The biggest risk of AI review is not that it misses things, but that it's too noisy - training people to ignore even its warnings; fewer but more accurate suggestions are far better than more but noisier ones.

My specific suggestion for Taiwanese readers: before introducing AI code review, think clearly about "what pain point you're trying to solve". If it's just to prevent PR congestion, choose a tool like cubic that's simple and automatically runs PR reviews, set it to "provide suggestions only, don't block merge", and observe its accuracy and noise level for a month before deciding whether to tighten the rules. Remember the order: first, plan your production (writing code) and review sides as a whole, don't just upgrade your writing speed and let review explode. For production tool selection, refer back to our AI Coding Agents Landscape; for supporting multiple models, controlling costs, and monitoring, see LLM Infrastructure Tools.

References

• cubic official website: https://cubic.dev
• CodeRabbit official documentation: https://docs.coderabbit.ai

This article is a summary of tool categories and introduction workflows, and the functionality and pricing of each tool are updated quickly. Please refer to the official latest announcements for their actual capabilities.