The prompt

Citation Check Against the Source Text guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when investigating a topic or preparing a decision-ready brief and you want a response that is easier to evaluate and act on.

text prompt
{ "model": "jev-latest", "state": { "claim": "{{claim}}", "cited_section": "{{citedSection}}" }, "questions": { "relation": { "type": "choice", "instructions": "How does the cited section relate to the claim?", "criteria": { "supports": "The section states the claim or directly implies that it is true", "contradicts": "The section states the opposite of the claim or implies it is false", "says_nothing": "The section does not address what the claim asserts, either way" } }, "overstated": { "type": "noul", "instructions": "The claim is stronger, broader, or more certain than what the section actually says" } } } How to run it: before calling the model, check in code that any quoted words in the claim appear verbatim in the section after whitespace normalisation. A missing quote is a fabricated citation and needs no model call. Then map relation.choice to a verdict: supports becomes verified, contradicts becomes contradicted, says_nothing becomes unsupported. Accept a verdict automatically only when relation.confidence is 0.8 or above, and flag overstated above 0.6 as a separate warning even when the citation is verified. Start the threshold high and lower it as you see how the model does on your own documents.

Tune the prompt, not the plumbing.

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Customized prompt1243 characters
{ "model": "jev-latest", "state": { "claim": "a specific claim", "cited_section": "a specific cited section" }, "questions": { "relation": { "type": "choice", "instructions": "How does the cited section relate to the claim?", "criteria": { "supports": "The section states the claim or directly implies that it is true", "contradicts": "The section states the opposite of the claim or implies it is false", "says_nothing": "The section does not address what the claim asserts, either way" } }, "overstated": { "type": "noul", "instructions": "The claim is stronger, broader, or more certain than what the section actually says" } } } How to run it: before calling the model, check in code that any quoted words in the claim appear verbatim in the section after whitespace normalisation. A missing quote is a fabricated citation and needs no model call. Then map relation.choice to a verdict: supports becomes verified, contradicts becomes contradicted, says_nothing becomes unsupported. Accept a verdict automatically only when relation.confidence is 0.8 or above, and flag overstated above 0.6 as a separate warning even when the citation is verified. Start the threshold high and lower it as you see how the model does on your own documents.

Useful structure, room to move.

Citation Check Against the Source Text guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when investigating a topic or preparing a decision-ready brief and you want a response that is easier to evaluate and act on.

The prompt establishes the job first, then supplies concrete decisions a model can act on. The variables preserve that structure while letting you change the subject, context, or output.