Build a Production RAG Pipeline From Scratch (Hybrid Search, Reranking, Observability) | AI Engineer

Description

90% of RAG tutorials show you toy demos that fall apart in production. This video builds a real production-grade RAG pipeline with hybrid search, cross-encoder reranking, semantic caching, and full observability. The exact architecture companies like Notion and Stripe use to serve millions of queries. Timestamps: 00:00 - Why Most RAG Systems Fail 00:30 - The 6-Layer Architecture 01:48 - Data Ingestion and Chunking 03:18 - Embedding Model Selection 04:50 - Vector Database Selection 06:08 - Three-Signal Retrieval 07:46 - Generation Patterns 09:07 - Production Reality Check 10:42 - Observability Stack 12:07 - What's Next What you'll learn: - The 6-layer production RAG architecture - Parsing tools compared (Unstructured, LlamaParse, Docling) - Semantic vs contextual vs parent-child chunking - Embedding model decision framework (Voyage AI, OpenAI, Cohere, open-source) - Matryoshka embeddings for 90% storage savings - Vector DB selection (pgvector vs Pinecone vs Qdrant vs Weaviate) - Three-signal retrieval (dense + sparse + reranking) - Reciprocal rank fusion explained - Citation enforcement and tiered model routing - Semantic caching (40% query reduction) - NLI-based hallucination detection - Observability with Langfuse, LangSmith, Arize Phoenix This video moves beyond basic demos to build a production-grade retrieval augmented generation (RAG) pipeline, tackling common issues like llm hallucination and retrieval misses. We explore a 6-layer rag architecture incorporating hybrid search rag and advanced context assembly for robust performance. Learn how to implement effective rag system design and optimize your llm pipeline for real-world applications. Based on "The AI Engineer's System Design Interview Guide" by Lamhot Siagian. This is Video 1 of a 10-part Production RAG series. Subscribe to catch them all.