AI Hallucination
AI hallucination occurs when an AI model generates plausible-sounding but factually incorrect, fabricated, or nonsensical information that is not grounded in its training data or provided context.
In Depth
Hallucinations are the primary trust risk when deploying AI in customer support. An AI agent that confidently states an incorrect return policy, invents a tracking number, or fabricates product specifications can cause real business damage and erode customer trust. Hallucinations occur because large language models are probabilistic text generators — they predict likely next tokens based on patterns, not facts.
They can produce fluent, confident-sounding text about things that are simply untrue. Mitigation strategies include Retrieval Augmented Generation (RAG) that grounds responses in verified knowledge bases, output validation that checks claims against source data, confidence scoring that escalates uncertain responses to humans, and constrained generation that limits responses to approved templates for high-risk actions. GuruSup employs multiple hallucination prevention layers: RAG with verified knowledge sources, action-level guardrails that prevent unauthorized operations, confidence thresholds for human escalation, and continuous monitoring that flags anomalous responses for review.
Related Terms
RAG (Retrieval Augmented Generation)
RAG is a technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer, ensuring responses are grounded in accurate, up-to-date data.
AI Agent
An AI agent is an autonomous software entity that perceives its environment, makes decisions, and takes actions to achieve specific goals without continuous human intervention.
Prompt Engineering for Support
Prompt engineering for support is the practice of designing and optimizing the instructions, context, and constraints given to AI language models to produce accurate, on-brand, and helpful customer support responses.
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