# What is Hallucination? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Limitations  
> **Canonical Citation:** [https://spidits.com/ai-glossary/hallucination](https://spidits.com/ai-glossary/hallucination)

## Definition
Hallucination is a phenomenon where a Large Language Model (LLM) generates outputs that are factually incorrect, nonsensical, or ungrounded in real-world data. It occurs because LLMs predict word probabilities rather than referencing a direct database of facts.

## Architectural Mechanics & Deep Dive
A hallucination is a phenomenon where a Large Language Model generates output that is factually incorrect, nonsensical, or ungrounded in real-world data, while presenting it with high confidence. Hallucinations arise because LLMs are trained to predict the most statistically probable next token based on training distributions, not to verify truth. Mitigating hallucinations requires grounding techniques like RAG, fact-checking APIs, search integrations, and safety alignment filters.

## Industry Applications & Real-World Use Cases
Output verification, prompt safety filters, and grounding checks

## Frequently Asked Questions

### Why do LLMs hallucinate?
Because they are designed to prioritize fluent, human-like generation based on statistical probabilities rather than lookup facts.

### How can you reduce hallucination?
By using techniques like RAG (Retrieval-Augmented Generation), self-reflection prompts, and strict system instructions.

## Related Terminology
- [llm](https://spidits.com/ai-glossary/llm)
- [rag](https://spidits.com/ai-glossary/rag)
- [vector-database](https://spidits.com/ai-glossary/vector-database)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*