RAG: Grounding AI Answers in Your Own Data

Retrieval augmented generation (RAG) pairs a language model with a search step. Instead of relying only on training data, the system embeds your documents into a vector store, retrieves the top matches for each question, and passes them into the prompt as context. The model then answers from those passages rather than guessing.

This solves two concrete problems: stale knowledge and hallucination. Because retrieval happens at query time, you update answers by re-indexing files, not by retraining. And when you return the source chunks alongside the answer, users can click through and verify every claim, which matters for support docs, legal text, and internal wikis.

Quality lives in the retrieval layer, not the model. Chunk documents at 200-500 tokens with slight overlap, add metadata filters so you narrow before you rank, and re-rank the top 20 down to 5 before prompting. On CinderHub you can wire a knowledge base into a chat model this way and test different chunking and embedding settings side by side before shipping.