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.
- 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.