CodeRabbit: how to use AI code review
Learn what CodeRabbit does, set up AI code review, understand pricing and privacy, and decide when it helps your team or adds more work.
In-depth explanations, practical guides and independent analysis. Choose a topic and format to find your next read.
Guides show how. Explainers unpack a concept. Commentary makes an argument. Explore Production notes.
Learn what CodeRabbit does, set up AI code review, understand pricing and privacy, and decide when it helps your team or adds more work.
A blocked agent can leave a valid change behind. Reconcile in-flight writes, stale approvals and queued work before resuming production operations.
Understand Meta Muse, its approval boundaries and a low-risk workplace trial. Check permissions, uncertain outcomes and data requirements before expanding access.
Size Collector queues, distinguish retries from loss, and rehearse recovery without mistaking delayed telemetry for current service health.
Keep unknown write results visible, preserve operation identity and rehearse a lost response with a local Python fixture.
A zero fallback can hide missing telemetry. Use a small PromQL test matrix to check what your error-rate alert can and cannot establish.
Plan an inference-service node drain with a scoped disruption budget, replacement-capacity checks and a practical maintenance worksheet.
Build a prompt-caching pilot that counts retries, failed work and unknown charges, with a local calculator and sample ledger.
Build repeatable cases and check recovery independently, with a runnable Python grader.
Check agent status, terminal persistence, and recovery before adopting Herdr.
Separate a completed task from evidence that the underlying risk was reduced.
Follow evidence through technical failure and incident response, then test whether the proposed correction changes the mechanism.