Definition

What is an AI Hallucination?

Last updated

A confidently-stated but factually incorrect output produced by a language model when it lacks grounded context for a claim.

Hallucinations occur when a model fills gaps in its knowledge with plausible-sounding text instead of deferring or retrieving. The most effective mitigation is context engineering: giving the model the specific, typed, current information it needs to answer, and a clear way to say 'I don't know.' Wire reduces hallucinations by exposing structured containers as MCP tools, so agents retrieve rather than guess.

Related Context Engineering RAG (Retrieval-Augmented Generation) Structured Context

Further reading

Articles about AI Hallucination

GPT-5.4-pro hallucinates more than GPT-5.4-nano

Vectara's 2026 benchmark shows OpenAI's flagship GPT-5.4-pro hallucinates at 8.3% while its nano variant stays at 3.1%. The reasoning-model tradeoff, explained.

How context engineering reduces AI hallucinations

Most AI inaccuracies in production are context quality failures, not model fabrications. Here's the research on what context engineering actually changes.

Why AI Hallucinations Are a Context Problem

GPT-5.2 hallucinates at 10.8%, o3-pro at 23.3%. The fix has less to do with better models and more to do with context engineering. Here's the research.

All terms

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AI Agent AI Hallucination AI Second Brain Context as a Service Context Compression Context Container Context Drift Context Engineering Context Portability Context Poisoning Context Rot Context Window Fine-Tuning MCP Server MCP (Model Context Protocol) Multi-Agent System Prompt Caching Prompt Engineering RAG (Retrieval-Augmented Generation) Semantic Search Structured Context Wire

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