Verify Claims, Citations, Code, and Summaries
Use different verification methods for different kinds of generated output.
In this lesson, you will learn to:
- Choose a verification method that matches the output.
- Reject citation-shaped text that does not support the claim.
Verify Claims, Citations, Code, and Summaries
A practical checking lesson covering source quality, citation existence, source-to-claim fit, calculations, code tests, summary fidelity, and uncertainty.
Verification is a ladder
The lowest rung is internal consistency: does the response contradict itself? Next, compare it with information you already trust. Stronger verification opens the cited primary source, confirms the source exists, checks the date and scope, and determines whether it supports the exact claim. For high-consequence decisions, reproduce the calculation or test, consult qualified expertise, and seek independent evidence.
Do not ask the same model “Are you sure?” and treat agreement as verification. The model may repeat the same unsupported pattern. A search snippet is not the source, and several articles may all copy one original mistake. Prefer laws, standards, official statistics, research papers, product documentation, and direct records when appropriate. Record what you actually checked.
Inspect the relationship between source and claim
A citation can be real yet irrelevant. Open it and ask whether it says what the generated sentence claims, whether qualifications were omitted, whether the data applies to the right population and date, and whether the quoted wording is accurate. Watch for invented authors, titles, page numbers, digital object identifiers, and links that lead to a different document.
For summaries, compare against the complete source and specifically inspect names, dates, negation, exceptions, confidence, and recommendations. For code, use tests, static analysis, peer review, and a safe environment. For calculations, recompute with a calculator or script and inspect units. Verification should target the ways that output can fail, not simply reread the prose.
State uncertainty where it changes the decision
A useful answer can include uncertainty. Distinguish observed facts, source claims, model-generated inferences, and your own assessment. If evidence is incomplete, narrow the statement rather than filling gaps. “I found no confirmation in the sources checked by 23 August 2026” is different from “it never happened.”
Preserve competing explanations when they matter. Ask what new evidence would change the conclusion. Avoid numeric confidence that has no defined basis. If the decision cannot tolerate the remaining uncertainty, pause and obtain better evidence or use a non-AI method. Verification is complete when the evidence is strong enough for the consequence—not when the output sounds certain.
Resources
- NIST AI RMF: Measure — Use the NIST playbook’s measurement prompts to think about testing, limits, monitoring, and documentation.