What is artificial intelligence prompt evaluation?
Definition
Artificial intelligence prompt evaluation runs defined prompts against representative cases and measures the resulting output. Criteria can include factual accuracy, relevance, format compliance, safety, latency and cost.
Evaluation should cover several inputs and repeated runs because model behavior can vary. Comparing prompts across model versions and providers helps teams avoid coupling product quality to one configuration. Automated scoring is useful, but important subjective criteria still benefit from human review.
It can measure correctness, relevance, instruction following, formatting, safety, latency, token use and cost on cases that represent the real application.
Why evaluate the same prompt across multiple models?
Models interpret instructions differently and have different speed, cost and reliability. Cross-model evaluation identifies which combination best meets the product requirements.
