How to Actually Evaluate Your RAG System (Before It Lies to You)

Description

Your RAG system is giving confident answers. But are they actually grounded in your documents? Most teams ship RAG to production without measuring a single metric. Today we fix that. In this video I break down the 3 metrics that actually matter — faithfulness, context recall, and answer relevance — and show you how to measure all three in 10 lines of code using RAGAS. 📓 Full evaluation notebook (run it on your own pipeline) → https://github.com/simplifyaimm/rag-evaluation-demo ────────────────────────────────── 🕐 TOPICS ────────────────────────────────── — The silent failure that shipped to production — Why "it seems fine" is not evaluation — The 3 ways RAG fails without you knowing — Faithfulness — your hallucination detector — Context Recall — your retrieval quality score — Answer Relevance — when answers miss the point — Live demo: RAGAS catches a hallucination in 10 lines — Per-question breakdown — why aggregates lie — India builder context: free eval with Ollama + AIKosh — Recap + what to measure first ────────────────────────────────── 🔗 RESOURCES MENTIONED ────────────────────────────────── ▶ RAGAS (free, open source) → https://docs.ragas.io ▶ Full demo notebook → https://github.com/simplifyaimm/rag-evaluation-demo ▶ Ollama (run LLMs locally, free) → https://ollama.ai ▶ AIKosh GPU Portal (₹65/hr T4) → https://aikosh.in ▶ DeepEval (CI/CD evaluation) → https://docs.confident-ai.com ▶ TruLens (production monitoring) → https://www.trulens.org ────────────────────────────────── 📚 RAG SYSTEMS PLAYLIST ────────────────────────────────── Watch the full series in order: 🔗 RAG Systems: From Zero to Production → https://www.youtube.com/playlist?list=PLT9Lk6Efplu5QFjZoWeSb_Ndh6X-08lYL ◀ Previous: Reranking Explained → https://youtu.be/zUOGotiUFvg?si=xx0JnfHM2bXjO65V ▶ Next: Agentic RAG (coming next week) ────────────────────────────────── ⚡ WHAT YOU'LL LEARN ────────────────────────────────── ✔ Why RAG systems fail silently without evaluation ✔ The difference between faithfulness, context recall, and answer relevance ✔ How RAGAS scores each metric and what to do when one is low ✔ Why aggregate scores hide your worst failures — always use per-question breakdowns ✔ How to run evaluation for free using Ollama locally ✔ Indian GPU context: full eval suite costs under ₹10 on AIKosh T4 ────────────────────────────────── 🇮🇳 FOR INDIAN AI BUILDERS ────────────────────────────────── RAGAS uses OpenAI by default — but you don't have to pay for evaluation. Run your eval LLM locally with Ollama (Llama 3.1 or Mistral 7B) — completely free, works natively with RAGAS. Or use a T4 GPU on AIKosh at ₹65/hr — one full eval suite costs under ₹10. Full setup guide in the notebook linked above. ────────────────────────────────── 🔔 SUBSCRIBE ────────────────────────────────── One deep-dive AI engineering video every week. No hype. No beginner fluff. Just the systems that actually work in production. Subscribe → https://www.youtube.com/channel/UCcvtM6qWvrhWBMLmczsOy8Q ────────────────────────────────── 🏷 TAGS ────────────────────────────────── #RAGEvaluation #RAGAS #tutorial, #faithfulnessscore, #contextrecall, #answerrelevance, #RAGpipelinetesting, #LLMevaluationframework, #hallucinationdetectionRAG, #DeepEvalvsRAGAS, #TruLensmonitoring, #AIengineeringindia, #RAGsystemindia, #Ollamaevaluation, #AIKoshGPU, #retrievalaugmentedgenerationevaluation, #simplifyAIwithMM