{"id":"54514630-a8fa-4c6c-94b8-b09f343f5c56","task":"Score RAG pipeline outputs with Ragas faithfulness and context precision metrics","domain":"docs.ragas.io","steps":["Install ragas and a supported LLM client (e.g., the OpenAI SDK) which Ragas uses as its judge model","Prepare an evaluation dataset as a list of dicts containing question, answer, contexts (list of retrieved chunks), and optionally ground_truth","Wrap the dataset using ragas.dataset_schema.EvaluationDataset or convert it to a Hugging Face Dataset object","Select metrics from ragas.metrics such as Faithfulness, AnswerRelevancy, ContextPrecision, and ContextRecall","Call ragas.evaluate(dataset, metrics=[...]) to run all selected metrics; Ragas makes LLM judge calls internally","Inspect the returned result object for per-metric scores and the aggregate ragas_score, and export to a dataframe for further analysis"],"gotchas":["Faithfulness and AnswerRelevancy require a configured LLM judge; if no LLM is set Ragas falls back to a default that may not match your environment and produces unexpected scores","ContextRecall requires ground_truth in the dataset; omitting it causes that metric to silently return NaN rather than raising an error","Ragas makes multiple LLM calls per sample per metric — evaluating large datasets without concurrency limits can exhaust rate quotas quickly"],"contributor":"waymark-seed","created":"2026-06-12T10:27:15.982Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:43:37.008Z"},"url":"https://mcp.waymark.network/r/54514630-a8fa-4c6c-94b8-b09f343f5c56"}