Retrieval & RAG
Build a RAG chatbot on your own documents
A RAG chatbot answers questions from your own documents by retrieving the most relevant passages first, then generating an answer grounded only in those passages, with citations back to the source. The demo below shows the whole pipeline: dense plus keyword retrieval, reranking, then a grounded answer you can trace.
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RAG Playground
Watch retrieval happen (dense + keyword + reranking), then a grounded answer with citations.
Why this matters now
A raw LLM will confidently invent answers about your private data, and a bigger context window does not fix it. Enterprises need answers over their own documents and tools that they can trust and audit, which means retrieval, strict grounding, and citations, not just a clever prompt.
How the reliable version works
- 1Chunk your documents and embed each chunk into a vector store.
- 2Retrieve with hybrid search: dense embeddings for meaning plus keyword search for exact terms.
- 3Rerank the candidates so the most relevant passages lead.
- 4Generate the answer grounded only in the retrieved passages, with citations.
- 5Refuse honestly when the answer is not in the documents, instead of improvising.
Common questions
How is RAG different from just using a long context window?
A long context window still forces the model to find the needle in the haystack, and cost scales with every token. RAG retrieves only the relevant passages, which is cheaper, faster, and easier to cite and audit. For most private-document use cases, retrieval beats brute-force context.
How do you stop a RAG chatbot from hallucinating?
Ground the answer strictly in retrieved text, require citations, and make "not in the documents" a valid answer. See the dedicated page on reducing AI hallucinations for the full approach.
Can it work over PDFs, wikis, and support docs together?
Yes. Anything you can turn into text can be chunked, embedded, and retrieved, including PDFs, Confluence or Notion wikis, and help-center articles.
Related demos, work and services
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