5 posts
RAG evaluation is a methodology that measures retrieval quality (recall@k, MRR, nDCG) and generation quality (faithfulness, answer relevance) separately. A layer-by-layer guide.
The four core metrics for measuring RAG systems: faithfulness, answer relevancy, context precision and recall. Evaluation with RAGAS, thresholds and context trust.
Building RAG is easy, proving it reliable is hard. Retrieval/generation metrics, reference-free evaluation with RAGAS, OpenTelemetry spans, and cost-per-successful-output.
Measure your RAG system's real quality with four core metrics: faithfulness, answer relevance, context precision, and recall. Ragas, LLM-as-a-judge, and Turkish challenges.
One of the most misleading quality failures in enterprise RAG systems is this: the system retrieves the correct file for a query, yet the final answer is still wrong, incomplete, or misleading. At first glance, this may look like a model failure, but the real issue often appears in the finer layers of the retrieval chain. Document-level correctness is not the same as evidence-level correctness. The system may find the right document, yet fail to retrieve the exact section that contains the answer, split meaning through poor chunking, overload the model with noisy context, miss the best passage because reranking is weak, or generate beyond the retrieved evidence. As a result, users face the frustrating question: if the right file was found, why is the answer still incorrect? This guide explains that problem end to end, covering the difference between document-level retrieval and passage-level evidence, chunking strategy, retrieval depth, reranking, context assembly, answer grounding, citation behavior, failure taxonomies, evaluation, and production quality loops.