Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or fabricated. In customer service, hallucinations can lead to wrong answers, broken trust, and real consequences for customers.
Why Hallucinations Happen
Large language models generate responses by predicting the most likely next word based on patterns in their training data. They don't look up facts in a database—they construct language. When a model encounters a question it doesn't have clear training data for, it doesn't say "I don't know." It fills the gap with something that sounds right. The result can be a confidently stated wrong answer that's nearly impossible for customers to distinguish from a correct one.
This is particularly dangerous in customer service, where a hallucinated refund policy, incorrect product specification, or fabricated troubleshooting step can cost real money and erode trust.
How Serious AI Agents Reduce Hallucinations
The most effective approach is retrieval-augmented generation (RAG)—grounding every response in verified source material rather than relying on the model's general knowledge. When an AI agent retrieves specific help articles, product documentation, or policy documents before generating a response, the risk of fabrication drops dramatically.
Multi-stage validation adds another layer: checking generated responses against source material before they reach the customer. The best implementations combine RAG with validation pipelines, confidence scoring, and clear escalation paths for low-confidence responses. The goal isn't eliminating hallucinations entirely—it's building systems where they're caught before they cause harm.