Coding involves a continuous interplay of logic, domain knowledge, and collaboration. Even small oversights—like an incorrectly returned HTTP status code or a missing test mock—can lead to confusion, bugs, or lost time. AI-based code review addresses these challenges by applying language models that go beyond basic static analysis. Instead of scanning for surface-level syntax errors alone, AI can interpret code context, compare it against project conventions, and highlight subtle inconsistencies.
In the following five scenarios, we illustrate how Baz’s AI code reviewer applies this deeper understanding to identify areas of improvement—from mismatched return statuses to variable naming mismatches—so developers can maintain clarity, reduce debugging, and keep their projects moving forward.
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 returning. 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 test mocks without looking at bash output
We make many API calls to GitHub and consistently mock it in our tests. In this change submitted, the developer received a failed test because he did not mock the relevant service correctly. I am familiar with the frustration of only after submitting a change request to see that you broke something else and have no clue what to look for. The AI code reviewer automatically extracted the change that caused the test to fail on the API call. The developer can go directly to the test and add the mock - and know when it was added! Once the fix is updated, the reviewer sees it in realtime, and sends the update in thread.
3) Reducing JWT Expiration – Avoiding GitHub API Errors
In this change, the developer initially set the JWT expiration (exp) to 10 minutes (600 seconds). The AI reviewer noticed that GitHub’s maximum allowed JWT expiration is 10 minutes, so using exactly 10 minutes can occasionally trigger API errors. The recommended fix was to reduce the expiration to 9 minutes (540 seconds), preventing any potential token invalidation. This small adjustment ensures reliability and consistency across the codebase, aligning with other references to GitHub’s JWT usage and avoiding any token-related failures.
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 code base context. Plus it suggests a better name that works within that broader context.
5) Improving your code conciseness
Writing concise code keeps your codebase readable, maintainable, and lean. 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 🔥
The examples presented here demonstrate how AI code review extends beyond simple checks to provide targeted, context-aware suggestions that significantly enhance developer workflows. By examining code logic in tandem with established patterns, AI can catch issues that might otherwise go unnoticed until much later.
This approach not only saves time but also promotes cleaner, more maintainable software. As these AI capabilities continue to evolve, development teams can rely on them to uphold coding standards, reduce friction in the review process, and ultimately produce more robust, consistent code.
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