Verify AI answers: the complete guide

Break it into separate factual claims and check each against a reliable source. To find which claims to prioritise, ask several AIs the same question first — the points where they disagree are the ones most likely to be wrong.

Any model can be confidently wrong. Comparing independent answers reveals alignment and divergence, but reliability comes from checking the claim against relevant, current sources and applying domain judgment.

Run the same question through several models independently, use divergence to prioritise checks, then verify consequential claims against authoritative sources. A unanimous panel can still repeat the same error.

Claim stated as one sentence, a primary source named, an exact quote and date from that source, and a verdict: confirmed, contradicted, or unverified if no locatable primary source settles it. Anything short of that is a summary, not a verification.

Yes — Satcove's free plan includes 3 cross-check queries a week on iPhone and the web, no card required.

Break the answer into factual claims, compare it with independent AI models, open the cited sources and verify important points against current primary evidence. A confident answer or unanimous panel is not proof of accuracy.

It measures how much several AI models align on the substance of an answer — their position, not their wording. High means strong panel convergence; low means material divergence to inspect before acting.

No. It only means the model outputs aligned. Models can share stale information, training patterns, or the same mistaken premise, so factual claims still require current evidence and, where relevant, professional review.

Because two answers can use entirely different words yet reach the same conclusion. Scoring the underlying position captures genuine agreement, which is what a decision depends on, rather than surface similarity.

On every Satcove verdict. Try it free on iPhone or the web — 3 queries a week, no card required.

A false statement an AI generates with the same confident tone as a true one — a fabricated citation, an invented statistic, a nonexistent case or source. It happens because the model predicts plausible text rather than retrieving verified facts.

Check any specific citation, statistic, or name against a real source. A practical shortcut is asking the same question to several independent AIs: a claim only one of them makes is the one most worth verifying.

Yes, every large language model can hallucinate, including the strongest ones — it's a property of how they generate text, not a defect unique to one brand.

Satcove asks six models the same question and shows exactly where they agree and where one diverges from the rest — a fast way to flag the outlier claim most likely to be a hallucination.

Each model is trained on different data and predicts likely wording rather than retrieving one fixed fact, so their answers diverge. The difference is normal, and comparing it across models tells you which parts of an answer are settled and which are not.

No. Disagreement usually marks a question that is genuinely contested, ambiguous, or at the edge of the models' knowledge. That is valuable: it points you straight to what deserves a closer look.

Satcove runs six models on your question and lays out their agreements and contradictions side by side, with a score — so the divergence becomes a clear, usable signal instead of noise.

Yes, Satcove's free plan includes 3 queries a week on iPhone and the web, no card required.