RAG Pipelines in Production: What Nobody Tells You

Retrieval-Augmented Generation sounds simple: retrieve relevant documents, feed them to an LLM, generate answers. In production, it's a different story. Here are the hard lessons from building RAG systems for enterprise clients. Chunking strategy is everything. Naive fixed-size chunks destroy context. We experimented with semantic chunking, document-structure-aware splitting, and overlap strategies. The optimal approach depends on your document types — there's no one-size-fits-all. Embedding model selection matters more than you think. We benchmarked OpenAI, Cohere, and open-source models across 10 use cases. The winner varied by domain: medical documents favored Cohere, code favored OpenAI, and multilingual content favored open-source. Retrieval quality is the bottleneck, not generation. We spent 80% of optimization time on retrieval: hybrid search (semantic + keyword), reranking with cross-encoders, and metadata filtering. Getting the right context to the LLM is the hard part. Evaluation is continuous. We built automated evaluation pipelines that test retrieval precision, answer faithfulness, and hallucination rates daily. Production RAG systems degrade as documents change — you need continuous monitoring. The biggest lesson: RAG is a system, not a feature. It requires infrastructure, monitoring, and ongoing maintenance. Budget accordingly.