Check before you pass it on
Check three statements against a fictional notice. Find the supported detail, missing date and assumption before passing it on.
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A working local example. Your changes stay in this page until you choose to save, copy or download. No AI model is called.
Separate claims from evidence
Authored community notice: The library plans a free introductory workshop. A date is not yet confirmed. Registration details will follow. Classify what the displayed sample supports, then inspect the explanation.
- Checked
- 0 of 3
- Matches the sample
- 0 of 3
Use the sample evidence: supported means it backs the claim; uncertain means it does not settle the claim; contradicted means it conflicts with the claim. These are fictional examples.
For the moments like this.
A message arrives with a helpful-sounding claim, but its summary may say more than the original notice. You want a straightforward way to check before passing it along.
What becomes possible
Compare three claims with a fictional library notice. Check what is stated, what is unconfirmed and what the notice does not support before passing a claim along.
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Sources, capability notes and how this example works
This local experience uses authored sample content and deterministic controls. Changing an input updates the example; no model runs, outside records are checked, or real-world actions are performed. Use the sources for the underlying AI capabilities and audience context, and the human check above when you apply the idea.
AARP: Navigating the World of AI ↗January 2026. Adults-50-plus use and hypothetical interest; broad AI is not only chatbots and interest is not purchase intent.
OpenAI: Does ChatGPT tell the truth? ↗Checked September 11, 2026. Answers can be incorrect or fabricated; original sources and human checks matter.
Research checked September 11, 2026. Access to specific AI features depends on your account, plan and region. Sample amounts, timings, scores and feedback illustrate the method rather than claim measured outcomes.