Build a RAG workspace over a document set
Stand up a working retrieval-augmented generation pipeline and see grounded answers cite their sources.
Install AnythingLLM Desktop and let it download its bundled local model on first run. Everything stays on your machine.
Create a new workspace — a workspace is one corpus plus its own settings, so keep this one for the exercise.
Upload 5–10 documents you know well: security policies, an incident write-up, vendor docs, a syllabus. Real text you can fact-check beats sample data.
Wait for embedding to finish. That step is chunking each document and converting each chunk to a vector — the "ingest → embed → store" half of the pipeline.
Ask a question the documents definitively answer. Open the citations on the reply.
Ask the same question again with the workspace set to ignore documents (or in a chat with no documents attached) and compare.
What to notice
- The cited chunks are the retrieval step — the model was handed those, it did not recall them.
- Without the documents the same question produces a vaguer, sometimes confidently wrong answer. That gap is the entire value of RAG.
- Answer quality tracks chunk quality. A badly formatted PDF retrieves badly, which is why ingestion is a security-relevant step, not just plumbing.