What makes a good RAG prompt?

It clearly separates instructions from retrieved context, numbers and attributes each chunk, tells the model to answer only from that context, and instructs it to say when the answer is not present rather than guessing. This template does all four.

Why instruct the model to refuse when the answer is absent?

Grounding the model in provided context only works if it is allowed to say I don't know. Without that instruction, models fill gaps with plausible-sounding but unsupported claims — the main failure mode of RAG systems.

How should I label sources?

Put a short label on the first line of each chunk, such as a title, URL, or document ID. The template carries that label into the citation so answers trace back to a real source the user can verify.

Is anything uploaded?

No. The prompt is assembled entirely in your browser. Your chunks and question are never sent to any server by this tool.

RAG Prompt Template Builder

RAG prompt template builder

Retrieval-augmented generation only works if the prompt wrapping your retrieved chunks is right. The model needs the context clearly separated from the instructions, each chunk attributed to a source, an explicit instruction to answer only from that context, and permission to say the answer is not present. Get any of those wrong and you get confident hallucinations. This tool assembles a correct RAG prompt from your chunks and question in one step.

How it works

You paste each retrieved chunk, optionally prefixed with a source label on its first line. The tool numbers every chunk, carries its label into the prompt, and wraps the whole set in a delimited context section. It adds a user question and a set of grounding instructions: answer using only the provided context, cite the source for each claim in the citation style you choose, and respond that the information is not available when it genuinely is not.

You pick a citation style — numbered like [1], bracketed source labels, or named inline — and the template adapts its instruction accordingly. Everything is assembled in your browser; nothing is uploaded.

Tips and notes

Keep chunks focused and labelled: a few tightly relevant passages beat a dump of marginally related text, and a clear label (URL, title, doc ID) is what makes a citation verifiable. Always keep the “say when the answer is absent” instruction — it is the single biggest lever against hallucination in RAG. If answers cite the wrong chunk, your retrieval is surfacing irrelevant passages, not your prompt; fix retrieval first. For multi-fact answers, numbered citations make it easiest to audit which claim came from which source. Finally, test with a question your context does not answer, and confirm the model declines rather than inventing — that is the real test of a grounded RAG prompt.