Building Production-Grade RAG Pipelines with LangChain

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building LLM applications that need access to external knowledge. In this post, I'll walk through the key patterns and lessons learned from building RAG systems in production. ## Why RAG? Large Language Models are powerful, but they have a fundamental limitation: their knowledge is frozen at their training cutoff date. RAG solves this by combining the generative capabilities of LLMs with real-time information retrieval. ## Architecture Overview A production RAG pipeline consists of several key components: 1. **Document Ingestion** — Processing and chunking documents 2. **Embedding Generation** — Converting text chunks to vector representations 3. **Vector Storage** — Storing embeddings in a vector database 4. **Retrieval** — Finding relevant chunks for a given query 5. **Generation** — Using retrieved context to generate responses ## Key Lessons ### Chunk Size Matters Finding the right chunk size is crucial. Too small and you lose context. Too large and you waste token budget. I've found 500-1000 tokens with 100 token overlap works well for most use cases. ### Hybrid Search is King Combining semantic search (vector similarity) with keyword search (BM25) significantly improves retrieval quality. This is especially important for domain-specific terminology. ### Evaluation is Non-Negotiable You need robust evaluation pipelines. Track metrics like retrieval recall, answer relevance, and faithfulness to build confidence in your system.