Eval & Observability
Complete guide to LLM evaluation and observability: automated evals, human feedback loops, A/B testing, and monitoring.
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After this section you can
- Design an eval stack across all four layers rather than a single offline score
- Build a golden dataset and an LLM-as-judge that survives scrutiny
- Wire prompt regression tests into CI and detect drift before users report it
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Evaluation & Observability — Complete Framework
The job description says "build high-performance evaluation pipelines." This is the differentiator between a demo and production. You must be able to design an eval system from scratch.
WHICH EVAL SECTION IS THIS?
This is the eval process & methodology — designing golden datasets, LLM-as-judge, A/B tests, CI regression, and drift detection. For the raw production metrics catalog and dashboards see Metrics That Matter. For grading agent trajectories specifically, see Grading Agents: Agentic Evaluation.
The Eval Stack (4 Layers)
Four layers — each answers a question the one below it cannot