RAG Pipeline Evaluation overview

Evaluates retrieval-augmented generation pipeline quality end-to-end — measuring retrieval precision@k and recall@k against ground-truth relevance judgments, scoring generation faithfulness and hallucination rates via LLM judges, benchmarking chunking and embedding strategies, profiling latency across retrieval and generation stages, and running regression suites before promoting pipeline changes. Excludes RAG architecture design and embedding model training.