Building Production RAG Systems: Lessons from the Trenches
Practical lessons from building Retrieval-Augmented Generation systems that actually work in production. Covering chunking strategies, embedding choices, retrieval tuning, and the failure modes nobody warns you about.
RAG (Retrieval-Augmented Generation) sounds straightforward: chunk your documents, embed them, retrieve relevant chunks, pass them to an LLM. In practice, there are a dozen places where this pipeline breaks down silently.
The Chunking Problem
Most tutorials recommend fixed-size chunking with overlap. This works until your documents have structure — sections, tables, code blocks — that gets arbitrarily split. Recursive character splitting is better, but for technical docs, semantic chunking (splitting at topic boundaries detected by embedding similarity) produces dramatically better retrieval.
Embedding Model Choice
text-embedding-ada-002 is not always the best choice despite its popularity. For technical domains, fine-tuned embedding models often outperform general-purpose ones significantly. Worth benchmarking with your actual data before committing.
Retrieval Tuning
Plain cosine similarity retrieval has a ceiling. Hybrid retrieval — combining dense vector search with BM25 keyword search — almost always improves recall on named entities, product IDs, and exact terms.
Re-ranking is the single biggest quality improvement I've found. After retrieving the top-20 chunks, run them through a cross-encoder re-ranker before selecting the top-5. The quality jump is often dramatic.
The Failure Mode Nobody Mentions
The most common production failure isn't retrieval quality — it's context window management. Retrieved chunks often contain redundant information, and naive concatenation wastes tokens and confuses the model. Deduplication and summarization of retrieved context before generation is underrated.
Build your eval harness before you build your pipeline. You can't improve what you can't measure.