Building a Production RAG Pipeline: Document Processing, Chunking, and Metadata Design

In the first article, we explored why many RAG systems fail in production and established a key principle: retrieval quality determines answer quality. We also introduced the architecture behind production-grade RAG systems and explained why a simple "embeddings + vector database + LLM" pipeline is rarely enough.

In this second part, we'll move one step earlier in the pipeline—to the moment a document first enters your system. We'll examine how documents should be ingested, cleaned, parsed, chunked, and enriched with metadata before a single embedding is generated. These decisions form the foundation of every production RAG system and often have a greater impact on retrieval quality than the choice of embedding model or LLM.

Production RAG Architecture Series

Building a Production RAG Pipeline: Document Processing, Chunking, and Metadata Design (you are here)

Beyond Vector Search: Building Better RAG Retrieval with Hybrid Search and Reranking

Building a Production RAG Pipeline: Document Processing, Chunking, and Metadata Design

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