What is AI Hallucination?

AI hallucination refers to instances where artificial intelligence systems generate information that sounds plausible and confident but is factually incorrect, fabricated, or not grounded in the source data. This is a critical concern for enterprise AI deployments.

Why AI Hallucinations Happen

• Training limitations: LLMs learn patterns, not facts
• Knowledge gaps: Model may lack specific information
• Ambiguity: Unclear prompts lead to invented answers
• Overconfidence: Models generate responses even when uncertain

Types of Hallucinations

• Factual errors: Incorrect dates, numbers, or details
• Fabrication: Made-up sources, quotes, or references
• Conflation: Mixing up similar but different concepts
• Extrapolation: Inventing details beyond available information

Risks in Customer Service

Hallucinations in support AI can cause:

• Wrong answers: Customers receive incorrect information
• Policy violations: AI makes unauthorized commitments
• Brand damage: Trust erodes with inaccurate responses
• Legal liability: Incorrect advice in regulated industries

Preventing Hallucinations

Enterprise AI platforms address hallucinations through:

• Retrieval Augmented Generation (RAG): Grounding responses in actual documents
• Citation requirements: Forcing AI to reference sources
• Confidence thresholds: Escalating when certainty is low
• Domain constraints: Limiting responses to known topics
• Human oversight: Review for high-stakes responses

Measuring Accuracy

Key metrics to track:

• Accuracy rate: Percentage of factually correct responses
• Grounding rate: Responses traceable to source content
• Escalation rate: Questions correctly routed when uncertain

Maven AGI Difference: Our Knowledge Graph grounds every response in your actual content. Maven cites sources, acknowledges uncertainty, and escalates when needed. Check achieved 85% accuracy rate because we prioritize correctness over confidence. That is enterprise-grade reliability.

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