Retrieval-augmented generation is more than embeddings and a vector store. The quality lives in chunking, ranking, grounding, and evaluation.
Most RAG failures aren't model failures — they're retrieval failures. If the right context never reaches the model, no amount of prompting will save the answer.
Chunking strategy matters more than model choice. Semantic, structure-aware chunking consistently outperforms naive fixed-size splits. Add a re-ranking step and you'll see another jump in relevance.
Grounding and citations aren't optional. Users trust answers they can verify. Every claim should trace back to a source, and the system should gracefully say 'I don't know' when retrieval comes up empty.
Enjoyed this?
Get our monthly engineering and AI insights, straight to your inbox.