OPRO Framework

A OPRO Framework is a meta-prompting framework that leverages LLMs to optimize prompts based on performance trajectorys.

• AKA: Optimization by Prompting, OPRO, Trajectory-Based Prompt Optimization Framework, LLM-Driven Prompt Optimization Framework.
• Context:
  • It can typically maintain optimization trajectorys tracking prompt performance across iterations.
  • It can typically generate prompt candidates using LLM reasoning about task requirements.
  • It can typically evaluate prompt effectiveness through validation metrics on test datasets.
  • It can typically implement meta-learning loops where LLMs learn from previous attempts.
  • It can often incorporate exploration-exploitation tradeoffs balancing novel prompts with proven strategys.
  • It can often utilize contextual bandits for prompt selection during optimization.
  • It can often apply convergence criterions determining optimization termination.
  • It can often achieve state-of-the-art performance with significant improvements over baseline prompts.
  • It can range from being a Simple OPRO Framework to being a Complex Multi-Stage OPRO Framework, depending on its optimization pipeline.
  • It can range from being a Greedy OPRO Framework to being a Exploratory OPRO Framework, depending on its search strategy.
  • It can range from being a Single-Metric OPRO Framework to being a Multi-Metric OPRO Framework, depending on its evaluation criterions.
  • It can range from being a Deterministic OPRO Framework to being a Probabilistic OPRO Framework, depending on its sampling method.
  • ...
• Examples:
  • Core OPRO Implementations, such as:
    • OPRO Framework for general prompt optimization.
    • OPRO-Chat Framework for conversational prompts.
    • OPRO-Code Framework for programming prompts.
  • Extended OPRO Frameworks, such as:
    • Hierarchical OPRO with multi-level optimization.
    • Distributed OPRO for parallel prompt search.
  • Domain-Specific OPRO, such as:
    • Medical OPRO for healthcare prompts.
    • Legal OPRO for legal document prompts.
  • ...
• Counter-Examples:
  • Gradient-Based Optimization, which uses mathematical gradients rather than LLM reasoning.
  • Random Search Method, which lacks intelligent trajectory.
  • Fixed Template System, which doesn't adapt prompts.
  • Manual Prompt Design, which requires human experts rather than automated optimization.
• See: Meta-Prompting Framework, LLM-as-Optimizer Technique, Performance Trajectory, Optimization by Prompting, APE Framework, Self-Supervised Prompt Optimization (SPO), Prompt Evaluation Metric, Meta-Learning, Trajectory Optimization, Contextual Bandit Algorithm.

---