Apr 17
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How to Evaluate RAG: Faithfulness, Answer Relevance & Context Recall Most RAG systems “look right” in demos. But break the moment real users start asking questions. Why? Because “seems correct” is not a metric. If you can’t measure your system, you can’t trust it. This video covers the three core metrics that tell you whether your RAG pipeline is actually production-ready: Faithfulness → Is the answer grounded in retrieved context? Answer Relevance → Does it actually answer the question? Context Recall → Did retrieval fetch the right documents? These three numbers help you catch: Hallucinations Off-topic answers Missing context Before your users do. You’ll also learn how to compute these using an LLM-as-judge, so you can evaluate your system at scale. 🚀 What You’ll Learn Why “it works” fails in production Faithfulness: grounding answers in context Answer relevance: matching user intent Context recall: evaluating retrieval quality How to measure all three using LLM-based evaluation ⏱ Chapters 0:00 Why you need RAG metrics 0:40 Faithfulness 1:40 Answer relevance 2:35 Context recall 3:20 Putting it together ⚡ Key Insight You can’t improve what you don’t measure. RAG systems need evaluation, not intuition. 🔗 Resources & Links 👉 Full course (AI Coding for PMs): https://maven.com/rajeshpeko/idea2prod 👉 Free weekly sessions: https://maven.com/rajeshpeko#lightning-lessons 👉 Instructor (Rajesh P): https://www.linkedin.com/in/rajeshpeko/ 👉 Want to move from demos to real AI products? Dyyota Enterprise Solutions builds production-ready agentic systems tailored to your use case: https://dyyota.com/contact How are you evaluating your RAG system today? #RAG #LLM #AIEngineering #AIPM #GenerativeAI #AIProducts #Evaluation #BuildInPublic