rag-evaluation-review

Reviews the retrieval and grounding evaluation for a RAG-based GenAI use case in a regulated financial-services firm. Confirms the corpus is fit for the intended purpose, retrieval quality is measured with named methods on a labelled set, grounding (faithfulness, citation precision, refusal on out-of-scope queries) is tested with documented metric semantics, failure modes are catalogued, mitigations are evidenced, and ongoing monitoring runs against a real ground-truth pipeline. Output is a second-line memo on whether the RAG implementation can be relied on for the use case's intended purpose, with named gaps, residual-risk framing, and owner actions.