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Plain-language AI glossary

Term 53TechniquesMeaning / context / connections

Techniques / Definition

AI Hallucination Detection

Techniques and systems for identifying when an AI model generates false, fabricated, or unsupported information that appears plausible.

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01 / Plain-language context

How AI Hallucination Detection works.

Hallucination is one of the biggest challenges in production AI. The model confidently states something that is entirely made up: a fake citation, a nonexistent API endpoint, or a fabricated statistic. Detection is critical for any application where accuracy matters.

Detection approaches include: (1) Grounding verification: compare the model's claims against source documents (used in RAG systems). If the answer contains information not in the retrieved documents, flag it. (2) Self-consistency checks: ask the model the same question multiple times. If answers vary significantly, confidence is low. (3) Confidence scoring: some models provide log probabilities per token. Low-probability tokens may indicate hallucination. (4) Fact-checking models: a second model or knowledge base verifies claims.

In production, combine multiple approaches. For RAG systems, check that every claim traces back to a source document and surface citations to users. For code generation, validate output by running it. For factual questions, cross-reference with trusted data sources. The key principle: never trust LLM output for high-stakes decisions without verification.

02 / Practical uses

Where it helps.

  1. 01Medical and legal AI applications
  2. 02RAG system quality assurance
  3. 03Automated fact-checking
  4. 04Financial report generation
  5. 05Customer-facing content verification
03 / Keep exploring
HallucinationWhen an AI model generates information that sounds plausible but is factually incorrect, fabricated, or not grounded in its training data.RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.AI GuardrailsSafety mechanisms that constrain AI system behavior, preventing harmful outputs, prompt injection, data leaks, and off-topic responses.Grounding (AI)Connecting AI model outputs to verifiable sources of truth: such as retrieved documents, databases, or real-time data: to reduce hallucination and increase factual accuracy.