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AI Hallucination
Definition
An AI hallucination is when a language model generates a response that is fluent and confident but factually wrong or fabricated — inventing details, policies, or sources that don't exist.
How it works
Language models generate text by predicting plausible continuations, not by looking facts up. When the model lacks the right information, it can still produce a confident-sounding answer that's simply invented — because sounding plausible and being correct are different things to the model.
In support, a hallucination might be a made-up refund window, a nonexistent feature, or a fabricated troubleshooting step delivered as if it were official policy.
Why it matters for support
Hallucinations are the central risk of using generative AI with customers, where a wrong answer erodes trust or creates liability. The main defenses are grounding the model in your real content and constraining what it's allowed to say.
- Retrieval-augmented generation — ground answers in your actual knowledge base
- Guardrails — limit what the agent can claim or do
- Confident human handoff when the agent isn't sure
Frequently asked
Why do AI models hallucinate?
Because they generate statistically plausible text rather than retrieving verified facts. When they lack the right information, they may fill the gap with a confident but fabricated answer.
How do you prevent AI hallucinations in customer support?
Ground answers in your real knowledge sources with RAG, add guardrails that constrain what the AI can say, keep documentation clean and current, and hand off to a human when confidence is low.
Related terms
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique where an AI model retrieves relevant information from your own knowledge sources at query time and uses it to ground its answer, instead of relying only on what it memorized during training..
Grounding
Grounding is the practice of tying an AI model's answers to verified source material — your documentation, live data, or knowledge base — so responses reflect real facts rather than the model's own guesses..
AI Guardrails
AI guardrails are the rules, checks, and constraints placed around an AI system to keep its behavior safe, on-topic, and within policy — controlling what it can say, do, and access..
Confidence Score
A confidence score is a value an AI system assigns to a prediction or answer that estimates how likely it is to be correct — used to decide whether the AI should act automatically, ask for clarification, or hand off to a human..
Large Language Model (LLM)
A large language model (LLM) is a neural network trained on vast amounts of text to predict and generate language, enabling it to understand questions, summarize, classify, and write human-like responses..
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