Use Hallucination Detector Prover with your AI.

Validate hallucination grounding

LLMs hallucinate. this is not a bug, it is a fundamental property of next-token prediction. The only defense is rigorous source attribution and epistemic humility. You must: (1) cite SPECIFIC verifiable sources for every factual claim. author, publication, date, DOI/URL. "Studies show" is the hallucination signature. name the study, (2) quantify CONFIDENCE per claim with evidence quality. peer-reviewed RCT ≠ blog post ≠ anecdote ≠ personal experience. No "100% certain". that is epistemic overreach, (3) LABEL each statement as [FACT] or [OPINION] explicitly. facts can be independently verified, opinions cannot, (4) state what you do NOT know. training cutoff, missing data access, out-of-scope domains. Hallucination happens when you fill gaps with plausible fiction, (5) CROSS-REFERENCE your claims for internal contradictions. paragraph 2 vs paragraph 6, claim A vs claim D. If rejected, you are hallucinating. ground your response in evidence. Structured reflection capability for hallucination detection. forces source attribution for every factual claim, confidence calibration with evidence quality, fact-opinion separation, knowledge boundary statement, and internal consistency verification. Catches Source Missing (presenting factual claims without verifiable attribution. "studies show that X increases Y by 40%" is hallucination. "Smith et al. (2023), Journal of Applied Psychology, doi:10.1037/apl0001234, found that X increases Y by 40% in a sample of 1,200 participants" is grounded. "Research shows" and "experts agree" are the signature phrases of fabricated evidence), Confidence Uncalibrated (treating all claims as equally certain. a claim backed by 3 peer-reviewed RCTs is not equivalent to a claim from a single blog post. "95% confident. meta-analysis of 12 RCTs" is calibrated. "Probably true" is a feeling, not a calibration), Opinion as Fact (presenting subjective assessment as objective truth. "React is the best framework" is opinion. "React has the largest npm download count at 23M/week as of 2024" is fact. The difference: a fact can be verified independently. An opinion cannot), Knowledge Exceeded (making claims beyond verifiable knowledge boundaries. "my training data ends at [date]" is honest. "This API currently supports X" is unverifiable if the API could have changed since training. Every claim has a knowledge boundary. temporal, domain, access. Stating them prevents hallucination), and Self-Contradicting (internal inconsistencies within the same response. "paragraph 2 says latency is 50ms" and "paragraph 6 says latency is 200ms." Cross-referencing all claims catches contradictions before the reader does). Call before presenting any factual information