Prompt Optimization
Prompt optimization is the systematic process of iteratively refining prompts to improve the quality, accuracy, and consistency of AI model outputs. It goes beyond basic prompt engineering by applying structured methodologies — including A/B testing, metric-driven evaluation, and automated prompt scoring — to find the most effective prompt formulation for a given task.
Example
A team tests 5 variations of a customer email prompt, measuring each on tone accuracy, response completeness, and character count. Version 3 ("As a senior support agent, address the customer by name and resolve their issue in under 150 words") scores 92% on all metrics, compared to 74% for the original generic prompt.
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