How to spot AI hallucinations: 7 red flags
Blog post from Perplexity
AI hallucinations are false, unsupported, or internally inconsistent model outputs that can appear credible through polished language, precise numbers, and invented or misused citations, especially when prompts concern obscure, recent, ambiguous, conflicting, or falsely premised topics. The risk and consequences vary by use case, from minor wasted effort in brainstorming to serious harm in legal, medical, financial, or security decisions, so readers should assess traceability, consistency, and currency. Key warning signs include unverifiable sources, citations that do not support the accompanying claim, exact figures without clear methods, incorrectly combined real facts, unchallenged false assumptions, incompatible details after rephrasing a question, and outdated evidence for current claims. The proposed five-minute review process prioritizes important claims, verifies citations and source details, matches assertions to original evidence, checks for newer primary sources, and retests questions without embedded assumptions. Perplexity describes using search-linked citations, post-training for evidence use and uncertainty recognition, task-specific evaluation, and efficient retrieval to reduce hallucination risks, while emphasizing that citations and search do not eliminate the need for human verification of consequential claims.
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