AI Glossaryfundamentals

Hallucination

When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or not grounded in its training data.

How It Works

Hallucinations are the biggest reliability challenge in AI applications. Models confidently cite non-existent studies, invent API endpoints, or fabricate statistics. Mitigation strategies: (1) RAG to ground responses in real data, (2) Web search for fact verification, (3) Structured outputs with source citations, (4) Temperature reduction for factual tasks, (5) Human review for critical applications. GPT-5 and Claude Opus 4.6 have significantly reduced hallucination rates, but the problem is not fully solved.

Common Use Cases

  • 1Understanding AI limitations
  • 2Designing safety guardrails
  • 3Quality assurance for AI outputs

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

Prompt Engineering

The practice of crafting effective instructions for AI models to produce desired outputs consistently.

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