RAG (Retrieval-Augmented Generation)

You ask your company chatbot about the current return policy. It answers confidently — and invents a deadline that does not exist. That is the hallucination problem. RAG (Retrieval-Augmented Generation) solves it.

How the architecture works

Documents are split into smaller chunks and stored as vectors — in Pinecone, Weaviate, or pgvector. When a query comes in, the search text is vectorized, the most relevant chunks are retrieved via similarity search, and fed as context into the LLM prompt. The model responds based on real sources, not from memory.

Reducing hallucinations by up to 96%

RAG cuts hallucination rates by 42-68%. A Stanford study with additional guardrails achieved 96% reduction. Hybrid search (vector + keyword) with reranking improves retrieval precision by 15-30%.

RAG vs. fine-tuning: when which?

RAG when current data is critical and sources must be citable. Fine-tuning when consistent domain knowledge needs to be baked into the model. In practice: both together. Fine-tuning determines how the model thinks. RAG determines what with.

RAG is the fastest path to a company chatbot. Documentation, product catalogs, internal guidelines — everything becomes searchable without training a model from scratch.