Our hallucination detection grades your model's outputs for fabricated facts, fake citations, and confident nonsense, so bad answers get flagged before they reach a customer.
We grade and flag hallucinations against your sources. We do not train models, run fine-tuning, or rewrite your product.
Hallucination detection is the process of checking AI-generated text against reliable sources to catch statements the model made up. It matters because models state false information with full confidence, and readers believe confident answers. Good hallucination detection combines source-grounding checks with human review, so a real person confirms the calls that automated checks cannot settle.
Not every hallucination is an obvious lie. The subtle ones do the most damage because nobody questions them.
Models invent paper titles, URLs, and case names that sound real. In research tools, legal assistants, and support bots, a fake citation is a trust killer. We verify that cited sources actually exist and say what the model claims they say.
A model that hedges on right answers and declares wrong ones with certainty is dangerous, because users trust the tone. We flag outputs where confidence and correctness part ways, so your team can see how often it happens and in which topics.
The trickiest hallucinations mix true and false details: the right company with the wrong revenue figure, the right law with the wrong year. Keyword checks miss these completely. Human graders with the source material in hand catch them.
A model that stayed grounded last quarter can start inventing facts after an update. Repeat grading on a fixed sample set shows whether factual grounding is holding steady or slipping, release by release.
You send 20 to 50 model outputs plus your source material, or we help you define the fact set.
Trained reviewers check every factual claim against sources, backed by automated grounding checks.
Sample-level grades, hallucination patterns, recommended fixes, and a walkthrough call.
The core problem with hallucinations is not that models make mistakes; it is that they present mistakes with complete confidence. A hesitant wrong answer gets questioned. A confident wrong answer gets copied into a report, a ticket reply, or a customer email.
Hallucination grading targets exactly this: the gap between how sure the model sounds and how right it is. We flag where confidence and correctness part ways, so you can see whether your system has a calibration problem or an occasional slip.
You cannot verify a claim against nothing. The strongest hallucination evaluations work from the source material the model should have used: your documents, your knowledge base, your approved facts. Every claim gets checked against that ground truth.
Where no source exists, we say so plainly in the report and grade against independently verifiable facts instead. The standard is always stated, never assumed, so you know exactly what a clean grade means.
Hallucinations are asymmetric: a thousand correct answers build trust slowly, and one confident fabrication can destroy it in a day. In support, research, and legal tools, a fake citation or a wrong number does not just embarrass the product; it makes users stop trusting every answer.
That is why hallucination detection is graded at the claim level, not the response level. A response that is ninety percent right and ten percent invented still fails the ten percent, and the report shows you exactly which claims broke.
Any statement not supported by the source material: invented citations, wrong numbers, misattributed quotes, and facts the model stated with confidence but got wrong. We quote the exact claim and explain why it failed.
It helps a lot. Send the documents the model should have answered from, and we grade every claim against them. If you have no sources, we grade against facts we can verify independently, and the report says exactly which standard was used.
Partly. Grounding and entailment checks help at scale, but the ambiguous cases need a person. Our reports mark which findings a human confirmed, so you know what was machine-flagged and what was human-verified.
They overlap. Hallucination detection asks whether the claims are true. RAG evaluation also asks whether the retriever found the right documents in the first place. If your system uses retrieval, grade both halves.
A fabricated fact that changes the meaning of the answer is P0. A wrong detail that does not change the meaning is P2. The scale is in the report band below, and every grade carries a written reason.
A regular error is a wrong answer the model could have gotten right with better reasoning: a math slip, a misread question. A hallucination is fabrication: invented facts, fake citations, details with no basis in anything. We grade them differently because the fixes differ. Reasoning errors point at prompts or model choice; fabrications point at grounding, retrieval, and citation checks.
Send a batch of outputs and get a hallucination report back in 2 to 3 business days once scope is confirmed.
Not sure where to start? Talk to us and we will point you at the right evaluation.
A 60-second tour of VideoEval scoring 10 AI-generated clips: prompt adherence, color saturation, motion ghosting, pass/fail verdicts, and the human review queue. Real demo, real numbers.
JudgeMyAI