# Recommended Reviewers Baz detects recurring feedback patterns in your team’s pull requests and converts them into reusable AI reviewers. These reviewers reflect your team's actual engineering practices - not generic lint rules-and are applied automatically to relevant parts of the codebase.

Code review agents that are based on your old PRs

### What Are Recommended Reviewers? Recommended reviewers are AI-driven checks derived from historical code review comments. Baz analyzes your past pull requests, clusters repeated feedback, and presents them as actionable reviewers that can be activated or tested before enforcement. Each reviewer includes: * A **reviewer prompt** (the rule logic) * **Code examples** from your own pull requests * **Suggested path scopes** using glob patterns * Associated metadata (category, repository, contributing authors) These reviewers surface the real-world habits and standards your team follows and let you codify them without writing a single rule manually.

Example reviewer

### Workflow Overview #### 1. Reviewers Automatically Generated Baz surfaces a list of Recommended reviewers under **Agents > Recommended Reviewers**. Each reviewer includes: * A title and category (e.g., *Code Style*, *API*, *Algorithms*) * The repository it originated from * Number of past discussions it’s based on Click any item to inspect the logic behind it. #### 2. Inspect Details You’ll see: * The **reviewer prompt** (used by the AI to evaluate future changes) * One or more **real PR comment threads** that led to its creation * A list of **suggested paths** scoped to the feedback (e.g., `baz-scm/bazai/**/*.py`) This gives you the full context before deciding whether to enforce it. #### 3. Test in Playground (Optional) Before enforcing a reviewer, you can test and refine it. * Select the reviewer and click **Test in Playground** * Choose a real open change request * Modify the prompt to fine-tune its behavior * Run it to preview AI-generated review comments in real-time This lets you calibrate the reviewer logic and test coverage before rollout.

Test in playground

#### 4. Activate Once you're satisfied with the scope and logic: * Click **Activate** * The reviewer will now run on new changes in the defined paths * Findings will appear as inline comments during review, tagged with the reviewer name ### Reviewer Playground The Reviewer Playground lets you validate and iterate on reviewer logic using real, in-context code diffs. Features include: * Editing and previewing prompts live * Selecting any open change request as input * Defining path scopes for enforcement * Seeing reviewer feedback exactly as it will be rendered during review Use it to test edge cases, tighten or broaden reviewer logic, or debug unwanted findings before activation. ### Path Scoping Each reviewer can be scoped to one or more repositories or subpaths. Baz uses glob syntax (e.g., `**/*.ts`, `src/**`) to define where a reviewer should apply. This allows you to: * Target reviewers to language-specific directories (e.g., `baz-scm/frontend/**/*`) * Exclude infrastructure or legacy paths * Apply different standards to different teams Scopes are editable from the UI prior to activation. ### Who Can Edit Only **organization admins** can activate, deactivate, or edit reviewers. All users can view reviewer prompts and test them in playground, but only admins can apply them to production workflows. ### Review Execution When a reviewer is active: 1. Baz detects if a file in the change matches its scope 2. The reviewer prompt is included in the AI review run 3. The AI evaluates the change and outputs comments tagged with the reviewer label Developers see this feedback inline and can resolve or respond as part of normal review flow. ### Use Cases * Standardize framework usage (e.g., “Use dependency injection in NestJS”) * Enforce architecture conventions (“Separate test and prod config”) * Catch performance antipatterns (“Avoid unbounded async chains”) * Ensure stylistic consistency (“Prefer Enums over magic strings”) These reviewers act as institutional memory, giving your team guardrails without needing to write static rules or onboard everyone manually. Let me know if you’d like tailored docs for your enterprise audience, a changelog-ready summary for release notes, or onboarding copy for first-time users. ## FAQ
What are Recommended Reviewers? These are AI reviewers derived from recurring patterns in your team’s past PR feedback. Baz surfaces them for review and potential activation.
How do I evaluate whether to activate a Recommended Reviewer? You can inspect its prompt logic, review sample PRs that inspired it, scope it with path globs, and test it in the Playground before activating.
Who can edit or activate Recommended Reviewers? Only organization admins can activate, deactivate, or adjust these reviewers. Regular users can view and test them but cannot enforce them.
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