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Plain-language AI glossary

Term 13FundamentalsMeaning / context / connections

Fundamentals / Definition

Hallucination

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

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Hallucination works.

Models can confidently cite nonexistent studies, invent API endpoints, or fabricate statistics. Mitigation depends on the job: retrieve trusted evidence, require source links, validate structured fields, constrain the task, test against known cases, and use human review where mistakes carry meaningful risk. Better models can reduce some errors, but no general-purpose model makes factual verification unnecessary.

02 / Practical uses

Where it helps.

  1. 01Understanding AI limitations
  2. 02Designing safety guardrails
  3. 03Quality assurance for AI outputs
03 / Keep exploring
RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.Prompt EngineeringThe practice of crafting effective instructions for AI models to produce desired outputs consistently.Fine-TuningThe process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.