RAG Evaluation

RAG evaluation studies whether systems find relevant evidence, use it faithfully, and produce correct, appropriately cited answers—including when they should abstain. Common approaches combine retrieval metrics such as Recall and nDCG with answer-level checks for correctness, coverage, faithfulness, citation support, and uncertainty; component ablations and error analysis help locate failures. Benchmarks increasingly test multi-hop and multi-turn questions, long or multimodal documents, domain-specific tasks, and noisy, conflicting, or adversarial evidence, alongside latency and resource costs. A central open tension is that better retrieval scores do not reliably yield better answers, while automated judges and metrics may be costly, unstable, or fail to transfer across datasets and domains.