Optimization provides a structured, iterative approach to refining AI-generated outputs by systematically improving prompts. Unlike experimentation, which focuses on testing multiple prompt variations, optimization enhances prompt by adjusting its structure based on evaluation-driven feedback.

This section covers:

  • Why optimization is essential for improving response clarity, consistency, and efficiency.
  • How optimization differs from experimentation and when to use each approach.
  • Step-by-step guidance on running optimizations using the Python SDK and the Future AGI interface.

Concept

Learn about optimization fundamentals and strategies

How To

Step-by-step guides for implementing optimization techniques

Vertex AIConcept