Chapter 08
Operating AI: Observability, Relevance, and Economics
Instrument the full AI pipeline: input, prompt assembly, retrieval, model call, tools, output filters, and user-visible result. Separate retrieval failures from generation failures in RAG. Use shadow mode, canary, rollback, and data contracts as measured production controls…
Apply this chapter
- Instrument the full AI pipeline: input, prompt assembly, retrieval, model call, tools, output filters, and user-visible result.
- Separate retrieval failures from generation failures in RAG.
- Use shadow mode, canary, rollback, and data contracts as measured production controls.
- Measure cost, p95/p99 latency, token use, and reliability as quality outcomes.
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Concepts in this chapter
- 051Observability and Tracing for AI SystemsYou cannot debug a final answer if you cannot see the path that produced it.
- 052RAG EvaluationRAG systems fail in two places: what they retrieve and what they say with it.
- 053Synthetic Test DataSynthetic data can expand coverage, but it can also manufacture a false picture of reality.
- 054Production Trace MiningThe strongest eval sets are often hiding inside production logs.
- 055Prompt and Policy VersioningMany AI regressions come from changing the instructions around the model, not the model itself.
- 056Canary, Shadow, and Rollback StrategyNon-deterministic systems should earn traffic gradually, with clear rollback rules.
- 057Cost and Token Budget TestingAI quality includes whether the system can afford to behave that way.
- 059Data Contracts for AI SystemsAI systems need explicit contracts for what they receive, produce, cite, log, refuse, and do.
- 060Operational Impact on Relevance and AI QualityQuality is what the user experiences at the end of the full system path, not what one isolated API reports before production reality gets…
- 061Token Efficiency, Model Choice, and Business ValueThe best AI system is not the biggest model or the cheapest model. It is the model path that creates the most trustworthy value for the risk…
- 193Modern EvalOps and AI Quality PlatformsModern AI evaluation is a lifecycle: datasets, tasks, scorers, experiments, traces, online monitors, human review, and release gates feeding…