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Agent Optimization provides a structured, iterative approach to refining AI-generated outputs by systematically improving prompts. With the agent-opt Python library, you can programmatically enhance your prompts by adjusting their structure based on evaluation-driven feedback. This library empowers you to move beyond manual trial-and-error, offering advanced algorithms to achieve higher-quality, more consistent, and more efficient LLM responses.

Why Use the agent-opt Library for Optimization?

The agent-opt library provides access to state-of-the-art optimization algorithms that go beyond simple prompt variations:
  • Advanced Algorithms: Access to 6+ distinct optimization strategies (Bayesian Search, Meta-Prompt, ProTeGi, GEPA, Random Search, PromptWizard).
  • Few-Shot Learning: Automatically select and format optimal examples for few-shot tasks.
  • Iterative Refinement: Systematic improvement through multiple rounds of evaluation and prompt modification.
  • Reproducibility: Programmatic control allows for versioning and tracking of optimization experiments.
  • Cost Efficiency: Smart evaluation strategies and targeted search methods help minimize API calls.
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.
  • Deep dives into each optimizer to help you choose the right strategy.
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