LLM Evaluation is the process of assessing how well a large language model (LLM) performs on tasks such as reasoning, factual accuracy, instruction-following, safety and tone. It uses benchmarks, curated test datasets, and human or automated (LLM-as-a-judge) scoring, both offline and on live production traffic.
In Voice AI, the LLM decides what an agent says and which actions it takes, so its quality defines the experience. Evaluating responses for accuracy, hallucination, policy compliance and brand tone keeps voice agents helpful, safe and on-message as prompts, models and tools change.