Retrieve
Break the document into useful passages, then find the few that best match the question.
Get a clear answer grounded in your source—with the exact passages used, or an honest “not enough information.”
Purchases over $500 need approval.[1]
Retrieval-augmented generation is easier to understand when you can see each decision. Groundquote keeps the important parts visible.
Read the plain-English RAG guide →Break the document into useful passages, then find the few that best match the question.
Give the model only the strongest passages and require evidence beside every claim.
Check citation IDs in code and refuse the answer when the source does not support it.
“Returns are accepted within 30 days with the original receipt.”
What should a trustworthy RAG system do?
Hybrid retrieval combines keyword precision with semantic similarity, then keeps the strongest, most distinct evidence.
The model receives a small set of untrusted source passages and must cite every answer. Unsupported answers are rejected.
Weak retrieval triggers a safe no-answer path instead of a confident guess. Your rating closes the evaluation loop.
Retrieval-augmented generation, or RAG, finds relevant passages in a source before creating an answer. Groundquote keeps those passages visible so you can check the result yourself.
Groundquote does not persist document text or questions in its database. In generated mode, the question and retrieved passages are sent to the configured model provider for that request.
Groundquote safely says it does not have enough information. That abstention is intentional—it is better than turning a weak match into a confident guess.
Paste text directly or upload Text, Markdown, CSV, or JSON files containing up to 60,000 characters. PDF parsing stays out of scope so the product remains small and transparent.
Review the retrieval pipeline, add an evaluation case, or suggest one focused improvement.