May 21
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RAG pipelines fail at retrieval 40% of the time. The model doesn't know. It takes the wrong context, generates a confident answer, and ships it — no error, no warning. EP#03 covered the two-pillar framework interviewers expect. EP#04 goes one level deeper — the 4-layer evaluation stack that catches the failures those two pillars miss. Chunking blind spots. Retrieval metric gaps. Faithfulness drift. Silent production degradation. And the compounding math that explains why a pipeline that's 95% accurate at every step still fails 23% of the time end to end. What you'll learn: Why 80% of RAG failures trace back to chunking — weeks before the first user query Fixed-size vs semantic chunking: faithfulness 0.47 vs 0.82, same model, same documents Context Precision vs Context Recall — two metrics that catch completely different failure modes RAGAS and LLM-as-judge: the production thresholds that actually matter (above 0.8, below 0.6) Hybrid search with BM25 and Reciprocal Rank Fusion — why vector-only retrieval misses 15 to 25% of answers Faithfulness scoring — catching the model when it generates beyond the context you gave it CI/CD quality gates, live evaluation on every query, and drift detection before users feel it The 77% reliability math: why 95% per layer across 5 layers isn't 95% end to end 🔗 Watch EP#03 first → RAG Evaluation at Scale: The Two-Pillar Answer Interviewers Expect: https://youtu.be/cMb5HTDFk1s?si=z4Vb26xjF1PKGSPe 🔗 EP#02 → LLM Throughput at Scale: The 4-Layer Answer Candidates Miss: https://youtu.be/cMb5HTDFk1s?si=z4Vb26xjF1PKGSPe 🔗 EP#01 → KV Cache Explained: The 4-Layer Fix Every AI Engineer Must Know: https://youtu.be/FioRSJU907Y?si=pdt3rmZQI-dfddES Key concepts: production RAG, RAG evaluation, RAG pipeline failure, RAGAS, context precision, context recall, faithfulness score, chunking strategy, semantic chunking, fixed-size chunking, hybrid search, BM25, Reciprocal Rank Fusion, reranking, Cohere Rerank, LangFuse, Phoenix, LLM-as-judge, hallucination detection, RAG observability, CI/CD quality gates, vector database, Pinecone, Weaviate, retrieval augmented generation, AI engineering, MLOps #ProductionRAG #RAGEvaluation #RAGAS #AIEngineering #GenAI #LLMEvaluation #RAG #MLOps #HybridSearch #GenAIInterviewSeries