Prompt Optimization
Systematic techniques for improving prompt effectiveness, including automated prompt search, A/B testing of prompt variants, and iterative refinement based on output quality metrics.
Why It Matters
Prompt optimization can improve LLM task performance by 20-50% without any model changes — it is the highest-ROI activity in LLM application development.
Example
Testing 50 prompt variants for a customer support bot, measuring resolution rate and customer satisfaction for each, and deploying the variant that scores highest.
Think of it like...
Like A/B testing headlines for an ad campaign — small wording changes can dramatically impact effectiveness, and systematic testing finds the winners.
Related Terms
Prompt Engineering
The practice of designing and optimizing input prompts to get the best possible output from AI models. It involves crafting instructions, providing examples, and structuring queries to guide the model toward desired responses.
Prompt Management
The practice of versioning, testing, and managing prompts used in LLM applications. It treats prompts as code that needs proper lifecycle management.
Evaluation
The systematic process of measuring an AI model's performance, safety, and reliability using various metrics, benchmarks, and testing methodologies.
Few-Shot Learning
A technique where a model learns to perform a task from only a few examples provided in the prompt. Instead of training on thousands of examples, the model generalizes from just 2-5 demonstrations.