Retrieval-Augmented Generation has become the standard approach for grounding LLM responses in factual data. But moving from a prototype to production-grade RAG is harder than most teams expect.
Chunking matters more than you think
The way you split your documents has a bigger impact on retrieval quality than your choice of embedding model. We’ve found that semantic chunking — splitting on topic boundaries rather than fixed character counts — consistently outperforms naive approaches.
Hybrid search is worth the complexity
Pure vector search misses exact keyword matches. Pure keyword search misses semantic similarity. Combining both with reciprocal rank fusion gives you the best of both worlds.
Monitor your retrieval quality
Set up evaluation pipelines that measure retrieval precision and recall on a golden dataset. Without measurement, you’re flying blind.