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.
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Learn what CodeRabbit does, set up AI code review, understand pricing and privacy, and decide when it helps your team or adds more work.
Plan an inference-service node drain with a scoped disruption budget, replacement-capacity checks and a practical maintenance worksheet.
Check agent status, terminal persistence, and recovery before adopting Herdr.
Clarify ownership across shared platforms, service reliability, and incident response.
When inference slows without obvious application errors, test physical constraints alongside queueing, workload changes, and software regressions.
Turn reliability policy into a release decision with fresh evidence, limited exceptions, and a clear path to resuming normal changes.
Read validated architectures as a starting point for workload, recovery, and support testing, not a promise of automatic reliability.
Use directory purpose, mount boundaries, and read-only checks to investigate missing files, full disks, and unexpected runtime state.
Measure a representative workload, identify the limiting resource, and test one reversible change instead of applying a universal sysctl recipe.
Use models for test suggestions and change analysis while keeping artifact identity, promotion rules, and rollback checks enforceable.
Choose representative traffic, define promotion criteria, and verify rollback compatibility before expanding a release.
Use operational state, visible defaults, and reversible transitions to evaluate complexity without discarding necessary capabilities.