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Ask the text.
Check the source.

Get a clear answer grounded in your source—with the exact passages used, or an honest “not enough information.”

Learn how RAG works
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RAG in about 60 seconds

Learn the recipe by using it.

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 →
01RAG step

Retrieve

Break the document into useful passages, then find the few that best match the question.

Try itAsk one specific question instead of asking for a full summary.
02RAG step

Ground

Give the model only the strongest passages and require evidence beside every claim.

Try itClick a citation to compare the answer with the original wording.
03RAG step

Verify

Check citation IDs in code and refuse the answer when the source does not support it.

Try itAsk about something missing and watch Groundquote safely stop.
Source passage

“Returns are accepted within 30 days with the original receipt.”

Quick challenge

“Are exchanges free?”

What should a trustworthy RAG system do?

Built for trust, not theater

Simple on the surface. Careful underneath.

01

Finds the right passages

Hybrid retrieval combines keyword precision with semantic similarity, then keeps the strongest, most distinct evidence.

02

Answers only from evidence

The model receives a small set of untrusted source passages and must cite every answer. Unsupported answers are rejected.

03

Knows when to stop

Weak retrieval triggers a safe no-answer path instead of a confident guess. Your rating closes the evaluation loop.

Good questions deserve clear answers

Before you paste a document.

What is retrieval-augmented generation?

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.

Does Groundquote save my document?

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.

What if the document does not contain the answer?

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.

Which files can I use?

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.

Open source and inspectable

Trust the product by reading the code.

Review the retrieval pipeline, add an evaluation case, or suggest one focused improvement.