What Is AI Hallucination?

AI hallucination is when a language model generates plausible-sounding content that is factually incorrect — fabricated citations, invented facts, made-up details, fictional sources. Hallucinations are an inherent property of probabilistic text generation, not a bug.

Frequently Asked Questions

Why do AI models hallucinate?

LLMs generate text by predicting probable next tokens. When asked questions outside their training data or about specific facts, they generate plausible-sounding tokens that are not necessarily true. They have no internal "I do not know" mechanism unless trained for it.

How can I reduce hallucinations?

(1) Use RAG (retrieval-augmented generation) to ground answers in specific sources; (2) ask the model to cite sources and verify them; (3) lower temperature; (4) use frontier models (GPT-5, Claude Opus 4) which hallucinate less; (5) ask the model to mark uncertainty; (6) verify key facts independently; (7) use structured outputs that force specific reasoning rather than free generation.

Are some models more hallucination-prone than others?

Yes. Generally, larger/newer models hallucinate less. Models with web-search integration (GPT-5 Search, Perplexity) hallucinate less for factual queries because they can ground answers in current sources. Models without internet access are at higher risk.

What is the most dangerous hallucination type?

Confidently-wrong specific claims. Examples: invented case citations in legal briefs (the Mavrek case), fabricated medical research citations, made-up code library functions. These are dangerous because they sound authoritative.

How do I detect hallucinations?

Verify claims against authoritative sources. For citations: check that the cited paper/case exists. For factual claims: cross-reference with reliable sources. For code: actually run it. For specific numbers: verify against primary sources.

Will hallucinations ever be eliminated?

Probably not entirely — they are inherent to probabilistic generation. They will continue decreasing as models improve and tooling matures (RAG, citation verification, fact-checking integration). But verifying important AI output will remain best practice.

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