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Topic
Deployment, tracing, model changes, capacity, spend, incidents, and production quality.
Practice items tagged with AI Production Operations.
Uses traces and saturation evidence to locate retry amplification, queue growth, exhausted workers, and failing fallback paths.
Uses traces to separate unclear goals, bad tool interfaces, missing state, contradictory results, poor termination, and retry amplification.
Diagnoses an AI regression by decomposing request stages, configuration changes, token growth, retries, routing, tools, retrieval, caching, and provider behavior.
Reproduces failing cases, compares versioned traces, isolates the first changed component, mitigates impact, and adds regression coverage.
Traces a missed answer through source ingestion, parsing, chunking, filtering, query construction, retrieval, fusion, and ranking.
Builds a reproducible evaluation harness that records immutable cases, versioned outputs, deterministic checks, calibrated graders, slices, and release decisions.