After implementing several Retrieval-Augmented Generation (RAG) systems in production environments, I have learned that the gap between a working prototype and a production-ready system is significant. Here are the key lessons I have gathered.
1. Chunking Strategy Matters More Than You Think
The way you chunk your documents can make or break your RAG system. I have found that semantic chunking—breaking documents at natural boundaries like paragraphs or sections—consistently outperforms fixed-size chunking.
2. Hybrid Search is Your Friend
Pure vector similarity search has limitations. Combining it with traditional keyword search (BM25) in a hybrid approach significantly improves retrieval quality, especially for domain-specific terminology.
3. Evaluation is Non-Negotiable
You cannot improve what you do not measure. Setting up proper evaluation pipelines with metrics like answer relevance, faithfulness, and context precision is crucial.
The field is evolving rapidly. Stay curious, keep experimenting, and always prioritize real-world performance over benchmark scores.