A fact-check prompt builder turns a vague “is this true?” into a disciplined evaluation an LLM can follow. Left to themselves, models tend to either rubber-stamp a claim or confidently reject it, often inventing supporting “evidence” along the way. This tool builds a prompt that forces the model to separate evidence from counter-evidence, name its sources, state caveats, and refuse to guess when it cannot verify — then optionally runs that prompt with your own API key.
How it works
You paste the claim exactly as stated, add context (date, region, source, domain) so the model evaluates the right version, and pick a required confidence level. The builder assembles an instruction block telling the model to list supporting evidence, contradicting evidence, key assumptions, and a final verdict on a fixed scale — and, crucially, to return “unverifiable” rather than fabricate facts when it lacks grounds. If you supply an OpenAI or Anthropic key, the prompt runs directly from your browser against the provider’s REST API; otherwise you copy it into any chat model. Nothing is sent to our servers, and your key is used only for the single request.
Tips and examples
For high-stakes claims (medical, legal, financial), set strictness to high and ask the model to cite specific, checkable sources — then verify those sources yourself, since models can cite plausible-sounding references that do not exist. Always provide context: “GDP grew 2%” needs a country and year before it can be judged. Use the counter-evidence section as your checklist — if the model cannot surface any opposing view, that often signals it is pattern-matching rather than reasoning. For live or recent claims, pair this with a model that has web search, because a base LLM only knows its training cutoff.