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AI glossary

AI Hallucination

An AI hallucination is when an AI produces information that sounds confident and plausible but is actually incorrect or entirely made up.

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In this guide

  1. 1What AI Hallucination means
  2. 2Why AI Hallucination matters

What AI Hallucination means

Language models generate text by predicting what fits well, not by checking facts. When the model lacks the right information, it may still produce a fluent, confident answer - one that simply is not true.

For example, ask an AI for a source on a niche topic and it might invent a realistic-looking article title and author that do not exist. The reply reads convincingly, which is exactly what makes hallucinations easy to miss.

Why AI Hallucination matters

Understanding hallucinations is essential for using AI responsibly. It teaches you to verify important output and to build tools that stay accurate.

It is why you should fact-check AI output that matters
Techniques like RAG reduce hallucinations by grounding answers
Clear prompts and good sources help limit made-up content
Knowing this helps you build trustworthy AI tools

Frequently asked questions

Give the AI accurate source material to work from, ask it to rely only on what you provide, and verify important facts. Techniques like RAG also ground answers in real, trusted data.

More AI terms

Zero-Shot LearningWhen an AI completes a task it was not given any examples for, relying only on its general training.Few-Shot LearningGuiding an AI by including a few examples in your prompt so it follows the pattern you want.Chain-of-Thought PromptingA prompting technique that asks an AI to reason step by step, improving accuracy on complex tasks.Prompt ChainingBreaking a task into several prompts where each AI step feeds its output into the next.AI AgentSoftware that uses AI to pursue a goal on its own - deciding the steps and taking actions to get there.Agentic AIAI systems that act with autonomy - planning, deciding, and carrying out multi-step tasks toward a goal.

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