An AI answer can be fluent, detailed and wrong. A citation can look convincing while pointing to a page that never makes the claimed statement. The useful habit is to treat the answer as a draft of things to check, especially when you are about to spend money, change settings or repeat a technical claim.
This source-checking workflow is part of the Techleez AI and technology guides. It complements the site’s responsible AI study guide by focusing on the verification step.
Turn the answer into a claim ledger
Copy only the factual claims into a short table. Separate facts from suggestions. “This app supports offline exports on the free plan” is checkable. “This app is ideal for you” depends on your needs and the evidence behind the comparison.
| Claim | Best evidence | Status |
|---|---|---|
| A feature exists on a specific plan | Current official plan documentation | Confirmed, contradicted or not found |
| A setting is available on your device | Official instructions for the version and your own interface | Matches or differs |
| A calculation is correct | Inputs, units and an independent calculation | Reproduced or unresolved |
| A study found a particular result | The actual paper and its limitations | Supported, overstated or unrelated |
Open the source and match the exact statement
Check who published the source, its date and whether it concerns the same product, region and version. A help page for a paid business account may not apply to a free personal account. A headline is not a substitute for the underlying evidence.
Follow citations to their original destination. If a link is broken, search the publisher’s own site for the document title. If you still cannot find it, label the claim unverified. Asking the same model to confirm its answer does not create independent evidence.
Separate “not found” from “false”
Suppose an AI says a note-taking app exports attachments and comments. Its official help page mentions exporting notes but says nothing about comments. You have not proved that comments are excluded; you have found a missing detail. Check a documented sample export or ask the provider before relying on it.
This distinction prevents overconfident corrections. A good final answer can say, “The export feature is documented, but preservation of comments is not confirmed.” That is more useful than a confident yes or no without evidence.
Reproduce low-risk examples
- Use sample data rather than private documents.
- Write down the expected result before testing.
- Check units, assumptions and edge cases.
- Compare the result with the original claim.
- Keep the evidence or a note of the unresolved limitation.
For a spreadsheet formula, try a normal value, zero and an empty cell. For software instructions, compare menu labels with your actual version. Do not run a terminal command you do not understand merely to verify an AI answer; read what it changes and consult official documentation first.
Raise the bar when consequences rise
NIST’s generative AI risk profile describes confidently presented false information and fabricated supporting material as important risks. An incorrect movie date and an incorrect medical or financial recommendation do not have the same consequences. For high-stakes decisions, use qualified professional judgment and authoritative, current sources rather than relying on this general workflow alone.
Keep a final ledger with three states: supported, corrected and unresolved. Remove unsupported claims from anything you publish. If the question itself depends on confidential material, use the AI data-upload checklist before sending it to a tool.
Source
Risk background checked on 28 October 2026 against NIST’s Generative AI Profile, AI 600-1. The claim-ledger examples are an editorial workflow, not a certification that a model or its outputs are reliable.