📊 RAG Evaluation

2 guides covering common problems, patterns, and production issues in RAG Evaluation.

RAG evaluation is how you measure whether a retrieval-augmented pipeline actually works — with stage-isolated metrics for retrieval and generation quality, golden datasets, LLM-as-judge, and CI regression gates.

  • Ragas metrics: faithfulness, relevancy, context precision/recall
  • Building and generating evaluation datasets
  • LLM-as-judge and its failure modes
  • Offline vs online evaluation and golden datasets
  • The tool landscape: Ragas, DeepEval, ARES, TruLens, LangSmith, Langfuse
Visit official site →
RAG Evaluation August 04, 2026 7 min

Beyond Ragas: A Practical Guide to Evaluating RAG and Agent Pipelines

LLM-as-judge, offline vs online eval, golden datasets, and how to choose an eval stack

Read guide →
RAG Evaluation August 04, 2026 5 min

RAG Evaluation with Ragas: Metrics That Actually Tell You If Your Pipeline Works

A practical, current-API guide to measuring retrieval and generation quality in RAG systems

Read guide →
Other Tools

Stay sharp as AI tools evolve

New guides drop regularly. Get them in your inbox — no noise, just signal.