Retrieval-augmented generation (RAG) is how AI assistants answer from company knowledge instead of guessing. Ask a question about a sample employee handbook and watch every step happen.
Documents are split into small passages so the most relevant pieces can be found. Highlighted chunks are the ones retrieved.
Your question is compared to every chunk. Real systems use embeddings; this demo uses a simplified word-weighting score.
Run a question to see the top matches.
Only the retrieved chunks are placed in the prompt, with instructions to stay grounded.
The assembled prompt appears here.
The model answers from the retrieved text and cites it. (This demo extracts the best-matching sentence.)
Update the documents and answers change instantly, with no retraining.
The model answers from real sources and can cite them.
No good match? A well-built system says so instead of guessing.
Retrieval respects which documents each user is allowed to see.
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