Building a RAG (retrieval-augmented generation) system
RAG is how you make an LLM answer from your data instead of making things up. Done well it is accurate and cites its sources; done badly it is a confident liar. The difference is entirely in the retrieval and evaluation, not the model.
Chunk, embed, and store with intent
Retrieval quality is set before the model ever runs. I chunk documents to preserve meaning, choose embeddings for the domain, and store them in a vector database (Weaviate, Pinecone) with the metadata you’ll actually filter on. On myzone.ai I built document-trained assistants on a vector DB and rebuilt search with fuzzy matching; on OptAI, pipelines that crawl and analyse pages for retrieval.
Retrieve, rerank, then ground
Naive top-k retrieval is where most RAG systems fail. I add reranking, metadata filters and query rewriting so the model sees the right passages, then ground the answer in them with citations — so users can trust it and you can audit it. Hybrid (keyword + vector) search catches what pure embeddings miss.
Evaluate, or you’re flying blind
I build evaluation sets that measure retrieval hit-rate and answer faithfulness, so you know the system works before launch and can catch regressions as your data grows. Caching and model routing keep it fast and affordable at scale.
- Retrieval quality — chunking, embeddings, metadata — decides RAG quality.
- Reranking, hybrid search and grounding with citations beat naive top-k.
- Evaluation sets for retrieval and faithfulness before and after launch.