Hallucination

A hallucination occurs when a large language model generates text that sounds plausible and confident but is factually incorrect, fabricated, or unsupported by its training data. The model is not intentionally lying -- it is producing statistically likely text that happens to be wrong.

How It Works

LLMs generate text by predicting the most probable next token based on learned patterns. They do not have a built-in fact-checking mechanism or a database of verified truths. When the model encounters a topic where its training data is sparse, ambiguous, or contradictory, it may fill gaps with plausible-sounding but fabricated details. This can manifest as invented API endpoints, nonexistent library functions, fictional citations, incorrect code syntax, or made-up statistics.

Hallucinations are particularly insidious because the model presents fabricated content with the same confidence as accurate information. There is no built-in signal that distinguishes a reliable response from a hallucinated one.

Why It Matters

For AI-powered coding assistants and technical tools, hallucinations can lead to real problems: broken builds, security vulnerabilities from incorrect configurations, wasted debugging time chasing nonexistent APIs, or production incidents caused by subtly wrong code. Recognizing that hallucination is an inherent property of current LLMs -- not a rare edge case -- is essential for building reliable AI applications.

In Practice

Several strategies reduce hallucination risk in deployed AI assistants. Retrieval-augmented generation (RAG) grounds responses in real documents rather than relying on the model's memory. Explicit instructions in the system prompt to say "I don't know" when uncertain can help. Temperature reduction decreases randomness and makes the model stick to higher-confidence outputs. Human review of critical outputs, automated testing of generated code, and logging model responses for audit are all practical safeguards in production environments.