A systematic approach to measuring whether your AI system is working — and catching regressions before they reach users. This section covers retrieval quality metrics, output evaluation, and production monitoring strategies.
What you will find here
- Retrieval metrics — recall@k, precision@k, MRR, NDCG, and when each metric tells you something useful.
- Output evaluation — LLM-as-judge patterns, reference-based scoring, and human evaluation frameworks.
- Regression testing — how to build a test suite that catches embedding model changes, prompt regressions, and configuration drift.
- Production monitoring — latency tracking, answer quality sampling, and alerting on distribution shift.
- Benchmarking — designing internal benchmarks that reflect your actual use case rather than academic tasks.
The goal is not to chase benchmark numbers, but to build confidence that your system behaves as intended across real queries.