Hallucination

When voice agents make up false information, invent policies, or provide incorrect facts during calls. In modern voice AI deployments, Hallucination serves as a critical component that directly influences system performance and user satisfaction.

Critical reliability issue - voice agents inventing return policies, quoting wrong prices, or making promises your business can't keep. Proper Hallucination implementation ensures reliable voice interactions and reduces friction in customer conversations.

Hallucination works by processing voice data through multiple stages of the AI pipeline, from recognition through understanding to response generation. Platforms like Hamming, Vapi, Retell AI each implement Hallucination with different approaches and optimizations.

Hamming AI's assertion testing specifically catches hallucinations where agents provide false information confidently. Their LLM-based evaluations verify factual accuracy and flag responses that deviate from ground truth.

Test for hallucinations using Hamming AI's approach: create test cases with known correct answers, validate agent responses against ground truth, monitor hallucination rates in production, and implement guardrails to prevent common hallucination patterns.