How do you implement RAG evaluation using RAGAS framework?

RAGAS (Retrieval Augmented Generation Assessment) provides automated metrics for evaluating RAG system quality. Metrics: (1) Faithfulness: measures whether the generated answer is factually consistent with the retrieved context. Decomposes the answer into individual claims and checks each against context using NLI. Score 0-1, higher is better. (2) Answer Relevancy: measures how relevant the generated answer is to the question. Generates questions from the answer and computes similarity with the original question. (3) Context Precision: measures the proportion of retrieved contexts that are actually relevant to answering the question. Higher precision means less noise in retrieved context. (4) Context Recall: measures whether all relevant information needed to answer the question was retrieved. Requires ground truth answers for comparison. Implementation: prepare an evaluation dataset with columns: question, groundtruth (reference answer), contexts (retrieved chunks), and answer (generated response). Run RAGAS evaluation to get scores per metric per question, then aggregate. Creating evaluation datasets: manually curate 100-200 question-answer pairs covering different topics and difficulty levels, use LLM-assisted generation (generate questions from documents, verify quality), and include edge cases (ambiguous questions, questions requiring multi-document synthesis, questions where the answer isn't in the knowledge base). Automation: integrate RAGAS evaluation into CI/CD pipeline, run evaluation after knowledge base updates or RAG configuration changes, track metrics over time, and alert on degradation. Beyond RAGAS: complement with human evaluation (periodic expert review of a sample), end-user feedback analysis, and domain-specific metrics (accuracy of extracted entities, correctness of cited sources). Use RAGAS metrics as initial automated quality gates but recognize they don't capture all dimensions of quality that matter to users.