AI is now a core part of the developer workflow - while the jury’s still out on if it’s increasing productivity or just increasing cognitive overload - what is definitely true is that AI code review can be an accelerator for the review process.
AI can enhance code review by automating bug detection, supporting code conventions, and improving overall code quality. As tools improve their ability to understand codebase context - (such as through extending the model by integrating code analysis results and tracing data) - they can prevent major breaking changes from being deployed.
At Baz, we dogfood our AI-enabled code review and recommendations everyday. As a team with a complex and multi-repo codebase, we need to understand more than just a diff change. Here are 5 real-world problems caught and resolved with AI code review.
1) Fix a Billing Response Issue – Integration incident between two internal services
In this change, a fix was submitted to fix an issue where a specific request between internal services was crashing the calling service. The easiest fix and the one that was submitted was to return something instead of an empty response. But what the AI reviewer caught was that this contradicts the 204 No Content status that’s being returned. The correct fix should have been on the calling service - and implementation was fixed to comply with this, before another developer had to invest time reviewing the change. The AI reviewer saved an entire "fix this" to "fixed" cycle.
2) Mocking Github API Rate Limits – Fixing tests without looking at bash output
We make many API calls to GitHub and consistently mock it in our tests. In this change, the developer broke a test because he did not mock the relevant service correctly. The frustration of seeing an unexpected test break only after submitting a change request and having no clue what to look for is a frustration I can relate to. Here, the AI code reviewer automatically found the change that caused the test to fail on the API call and what exactly failed. The developer can go directly to the test and add the mock - and baz reviewer can actually know when it was added, too!
Once the fix is updated, the reviewer sees it in realtime, and sends the update in thread.
3 Validating Redis Cache connection – A preventable copy-paste mistake
In this change, a Redis cache was added but due to copy-paste issues (it happens to the best of us) the port was not changed from the MongoDB port. This could lead to port issues, unexpected behaviors in the app, and a debugging nightmare if outages occur without knowing why.
These small details are critical and also unfortunately easy for developers to identify in large changes. This is an excellent use case for an energetic AI reviewer to find.
4) Managing new auth token variables – Better and consistent naming with AI
Maintaining code consistently is always a battle as new variables change meaning and function. Here a new variable name is added but the AI code reviewer recognizers this as misleading based on codebase context. Plus it suggests a better name that works within that broader context.
5) Improving your code conciseness to be a better developer
Writing concise code is essential to keeping your codebase readable, maintainable, and looking sharp. Here the AI reviewer is leveraging context, best practices, and code base conventions to identify and suggest a better way to implement this change. Almost like a secret weapon to make your code always look super 🔥
Make AI a tool that works for you in code generation
AI-powered code review is more than just a productivity boost—it’s a safeguard against costly mistakes, ensuring code quality, consistency, and security at scale. As we’ve seen in these real-world examples, AI can catch critical issues that might otherwise slip through manual review, helping teams ship better code with more confidence. You can run this automated review before submitting your code, and send your team the top quality code every time.
At Baz, we rely on AI to enhance our review process, ensuring that every commit aligns with best practices and avoids major pitfalls. If you’re looking to streamline your development workflow, it might be time to integrate AI into your code review process. 🚀
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