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Glossary

AI hallucinations

AI hallucinations refer to instances when an artificial intelligence system, like a large language model (LLM) or generative tool, produces outputs that appear coherent and plausible but are actually false or nonsensical. These mistakes are not due to software bugs but arise from the model predicting the most likely or fluent sequence of words based on patterns in its training data, without grounding in verified facts.

The name draws an analogy to human hallucinations: just as people might see things that aren’t there, AI “hallucinates” information that seems real but isn’t. This can happen with text, images, or other data types.

Why AI hallucinations occur

AI hallucinations happen because conversational AI tools are designed to predict the next word in a sentence, not to check facts. So if a prompt is unclear or missing details, the model might fill in the gaps with guesses that sound convincing but are wrong.

Bad or biased training data can also cause problems. If the model learns from content that’s inaccurate or slanted, it can repeat those mistakes. Because these models are very good at sounding confident, they can give wrong answers in a way that seems trustworthy. They also don’t naturally show doubt unless they're specifically designed to.

Open-ended questions can make hallucinations more likely, since the model may have to invent details to respond. This has led to real-world issues, like lawyers using fake court cases and tech AIs confidently stating made-up product features.

Impact on customer experience (CX)

AI hallucinations can hurt customer experience. When models provide false but convincing information, it can lead to: 

  • Reduced trust: When AI confidently asserts incorrect information, users lose faith in the entire system 
  • Brand damage: Misleading or false responses—even once—can harm brand reputation and customer loyalty.
  • Frustration & confusion: Receiving inaccurate guidance or directives leads to a poor customer journey and potential escalation to human agents.

AI continues to be central to CX through chatbots, virtual assistants, and intelligent routing, making hallucinations not just technical errors but key business risks.

AI hallucination mitigation techniques

The most effective way to deal with AI hallucinations is to prevent them in the first place. Below are some practical steps to help ensure your AI models deliver accurate and reliable outputs:

  • Augmented retrieval (RAG): Combine generative responses with real-time document or database look-ups. This anchors responses in facts. 
  • Confidence‑aware responses: Train models to say “I don’t know” when unsure, rather than fabricating answers. 
  • Data & model refinement: Improve training data quality, fine‑tune with human feedback, and apply post‑generation validation to catch hallucinations. 
  • Context integration: Incorporate user session context or enterprise knowledge bases to constrain model output.
  • Human‑in‑the‑loop: Use hybrid models where AI drafts replies but humans validate them before sending, especially for critical queries.

Some experts say “hallucination” isn’t the right word and liken it more to a guess based on patterns, not facts. Still, reducing these errors is a top priority, especially in customer experience, where accuracy and trust really matter. Risks can be limited by using tools like fact-checking layers, confidence-based refusals, and human review. Preventing hallucinations is key to creating reliable and trustworthy customer interactions.

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