Production RAG with Semantic Kernel: Patterns, Chunking, and Retrieval Strategies

Retrieval-Augmented Generation (RAG) is the pattern that makes LLMs genuinely useful for enterprise applications. Instead of relying solely on training data, RAG grounds responses in your actual documents, databases, and knowledge bases. In Part 3 , we explored memory and vector stores. Now we'll build production-ready RAG systems with proper chunking, retrieval strategies, and evaluation. The RAG Pipeline Every RAG system follows this flow: ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ INGEST │ -> │ INDEX │ -> │ RETRIEVE │ │ Load docs │ │ Chunk + embed│ │ Vector search│ └──────────────┘ └──────────────┘ └──────────────┘ │ v ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ RESPOND │ <- │ AUGMENT │ <- │ RANK │ │ LLM generates│ │ Build prompt │ │ Score + filter│ └──────────────┘ └──────────────┘ └──────────────┘ Let's build each component properly. Document Chunking: The Foundation Chunking is where most RAG systems succeed or fail. Too large, and you waste context window spac