--- title: "UX of AI Code Review Tools | Utkarsh Panwar" description: "The UX of AI code review tools, from designing CodeAnt AI: how review comments in a pull request earn trust through severity, evidence and easy dismissal." url: https://www.utkarshdesign.com/guides/ai-code-review-ux author: Utkarsh Dhairya Panwar updated: 2026-10-08 --- # AI code review UX that developers trust Guide · 6 min read · By [Utkarsh Dhairya Panwar](https://www.utkarshdesign.com/about) · Updated 8 Oct 2026 **Developers judge an AI code reviewer inside the pull request, so most of its UX is the comment. Each one needs a severity, the exact lines, a sentence or two of why, an applicable fix and an easy dismiss. Post fewer, higher-confidence comments and summarise the rest at the top.** ## The product is a comment in someone else's tool At CodeAnt AI I design the product around AI code review, alongside security scans, code quality and developer insights, used weekly by 40,000+ developers. The odd thing about designing an AI reviewer is how little of it is your own interface. Most of the experience happens in GitHub, GitLab, Bitbucket or Azure DevOps, in a comment box you don't control, next to comments from people the developer knows. That sets a high bar. A human reviewer who leaves ten nitpicks gets a quiet word from the team. An AI reviewer that does the same gets its comments resolved unread, and a week later someone asks in Slack whether it can be switched off. Lists of the best AI code review tools compare models and pricing; very few look at what the comment is like to receive. ## Anatomy of a comment that gets read 1. **A severity label first**: Bug, security, performance or style, in a fixed vocabulary. The developer decides whether to read on from the label alone, so it has to be honest. 2. **Anchored to the exact lines**: Posted on the line or range that causes the issue, not as a general remark on the file. If the cause is elsewhere, link to it. 3. **One or two sentences of why**: The consequence in plain words: what breaks, for whom, under what input. Skip the lecture on best practice. 4. **A suggested change**: In the platform's own suggestion format where it exists, so applying it is one click and the diff is visible before anyone commits. 5. **A way to say no**: Dismiss with a reason (false positive, intended, won't fix). The reason is feedback for the model and a record for the next reviewer. | Comment that gets resolved unread | Comment that gets acted on | | --- | --- | | "Consider adding error handling here to improve reliability and maintainability." | "Bug: if fetchUser returns null (deleted accounts), line 42 throws and the whole request fails with a 500. Suggested fix below." | | "This function is complex. Consider refactoring." | "Style, low: this function has 4 nested conditions. Skip if intentional." | | "Potential security issue detected." | "Security, high: user input reaches this SQL string unescaped (from req.query.id on line 18). Use a parameterised query." | I wrote those examples for this page; none of them come from a real tool. The pattern on the right is the one I push for: a label, a cause, a consequence, a fix. ## Fewer, better comments Volume is the fastest way to lose a developer. Every low-value comment trains them to skim the next one, and the one critical finding in a pull request gets resolved along with nine style notes. - Set a confidence bar for posting inline, and keep lower-confidence notes out of the diff. - Group repeats: one comment that says "the same issue appears in 6 other places" with links, instead of seven separate comments. - Never post style notes that a linter in the repo already enforces. - Let teams choose which categories post inline and which only appear in the summary. The summary comment at the top of the pull request is where the rest goes. It should say what was reviewed, what was found by severity, and what wasn't checked (generated files, files over a size limit, languages not supported). Silence about coverage reads as "all clear". ## Trust is earned on the first pull request In CodeAnt's onboarding, the first reviewed pull request is the point where a new team sees the product working (the onboarding story is in how CodeAnt doubled trial-to-paid). That moment is also the AI's first impression. If the first review is noisy or wrong, later accuracy rarely wins the team back. On a team's first few pull requests I'd be conservative and post only high-severity, high-confidence findings. Tell the developer what's happening while they wait, too, because a review that appears after the PR is merged is worse than none. ## Make dismissal easy, then learn from it Teams tend to treat a dismissed AI comment as a mistake to hide. I think it's the most useful signal in the product. A dismissal with a reason tells you the model was wrong, or the team has a convention the model doesn't know about, or the finding is real but not worth it here. - Make dismissal as easy as resolving, with a short reason picker rather than a free-text box. - Remember it. The same finding on the same pattern shouldn't come back next week. - Show teams their own numbers: comments posted, applied, dismissed. A tool that hides its hit rate looks like it has something to hide. ## Outside the pull request The pull request is for the author. Managers need the same findings rolled up across repos, ranked, with an owner on each, and that's a dashboard problem rather than a comment problem. I've written about that side in security dashboard design. For the broader principles behind AI output people can check, see designing AI features users trust. If you're building an AI developer tool and want the review experience designed with this much attention, that's what my AI and developer tools design service is for. [CodeAnt AI case study](https://www.utkarshdesign.com/work/codeant-ai): Onboarding, the admin wall and the product behind the review comments. ## Questions people ask ### What makes a good AI code review comment? A severity label, an anchor on the exact lines, one or two sentences on the consequence, a suggested change that can be applied in one click, and an easy way to dismiss it with a reason. ### How do you stop AI code review from being noisy? Post inline only above a confidence bar, group repeated findings into one comment, skip anything a linter already catches, and put lower-priority notes in a single summary at the top of the pull request. ### Should AI review comments look different from human ones? Yes. Developers should know at a glance that a comment came from a tool, so they can weigh it accordingly. Hiding that costs trust when the tool is wrong. ### Should an AI reviewer block merging? Only for findings the team has explicitly agreed are blocking, usually high-severity security issues. Everything else should inform the human reviewer, not overrule them. ## Sources 1. [Incorporating feedback in your pull request](https://docs.github.com/en/pull-requests/how-tos/review-pull-requests/incorporating-feedback-in-your-pull-request), GitHub Docs 2. [Suggest changes](https://docs.gitlab.com/user/project/merge_requests/reviews/suggestions/), GitLab Docs 3. [Lessons from Building Static Analysis Tools at Google](https://research.google/pubs/lessons-from-building-static-analysis-tools-at-google/), Google Research (Communications of the ACM) ## Working on this? Tell me what you're building and where it's stuck: [book a 30-minute call](https://cal.com/utkarsh-design/30min), email [work@utkarshdesign.com](mailto:work@utkarshdesign.com) or [send a brief](https://www.utkarshdesign.com/contact). ## Related - [Designing AI features users trust](https://www.utkarshdesign.com/guides/designing-ai-features) - [Developer tools UX lessons from CodeAnt](https://www.utkarshdesign.com/guides/designing-for-developer-tools) - [Security dashboard design for engineering teams](https://www.utkarshdesign.com/guides/security-dashboard-design) - [Product designer for AI and developer tools](https://www.utkarshdesign.com/services/ai-and-developer-tools-design) --- Utkarsh Dhairya Panwar, Founding Product Designer and Design Engineer (IIT Roorkee; Founding Designer at CodeAnt AI, YC W24). Email: work@utkarshdesign.com · Book a call: https://cal.com/utkarsh-design/30min · Hire: https://www.utkarshdesign.com/hire · About: https://www.utkarshdesign.com/llms.txt