Code Review Comments to Dev Standards: 4 Internal Code Prompts We Turned Into AI Reviewers

Every engineering team has unspoken rules, the kind of standards you’ve repeated in PRs a hundred times but never bothered to write down. They don’t show up in your CI, but you feel them every time a teammate misses them.

With AI code reviewers, those patterns don’t need to stay scattered. At Baz, we’ve been using our own tool to build Custom Reviewers that catch the stuff we care about. And it’s made our review process faster, clearer, and more consistent without sacrificing context.

In this post, we’re sharing four examples of Baz-style reviewers pulled from our own repos. Think of it as a starting prompt library for your team. Looking for prompts from the top open source repositories? Check out Awesome Reviewers here

1\. Explicit configurable parameters

Every team has a few hidden defaults that no one questions, until they break something. Timeouts, thresholds, and filters often get hardcoded or quietly inherited. We created a reviewer that flags missing parameters and encourages more maintainable, forward-compatible code.

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2\. Manage interactive states

Modern frontends are async by default, but review comments often miss subtle bugs like double-clicks, forgotten loading states, or misused React hooks. We trained a reviewer to flag missing state safeguards.

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3\. Explicit over implicit errors

When errors get swallowed, bugs slip by. Worse, reviewers get used to ignoring error handling altogether. Our internal Rust reviewer now flags silent panics, use of expect(), and error returns that fail to log anything useful.

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4\. Parameterize deployment configurations

CI/CD reviewers can do more than lint YAML. We built one that catches hardcoded infra values and flags scaling configs that might jeopardize availability.

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Try these prompts with Baz Custom Reviewers

These examples were built using Baz Custom Reviewers, a flexible way to turn your team’s review principles into reusable, testable AI reviewers. With Baz, you can:

• Write reviewers as system prompts or chain-of-thought reasoning
• Evaluate performance across different teams or services
• Track reviewer effectiveness over time using in-product evals
• Share and reuse reviewers across teams with full versioning and memory

You can try all of these examples and more in the Reviewer Playground, or start from your own internal patterns. It's like writing your team’s reviewer once, and never repeating the same comment again.

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