Meta-Prompting Framework

A Meta-Prompting Framework is a prompt optimization framework that uses LLMs as optimizers to generate and evaluate prompts in a self-referential optimization loop.

• AKA: LLM-as-Optimizer Framework, Self-Optimizing Prompt Framework, Meta-Optimization Prompt Framework, Recursive Prompt Optimization Framework.
• Context:
  • It can typically create meta-optimization loops with performance trajectorys tracking prompt improvement over iterations.
  • It can typically select top-performing prompts with iterative evaluation using validation datasets.
  • It can typically leverage LLM reasoning capabilitys to understand task requirements and generate appropriate prompts.
  • It can typically implement prompt mutation strategys guided by LLM understanding of task semantics.
  • It can often frame instructions as natural language programs with candidate scoring for prompt selection.
  • It can often maintain prompt history tracking evolutionary paths of optimization processes.
  • It can often reduce human intervention by automating prompt refinement cycles through LLM feedback.
  • It can often achieve convergence within multiple optimization rounds for standard NLP tasks.
  • It can range from being a Simple Meta-Prompting Framework to being a Complex Trajectory-Based Meta-Prompting Framework, depending on its optimization sophistication.
  • It can range from being a Single-LLM Meta-Prompting Framework to being a Multi-LLM Meta-Prompting Framework, depending on its model architecture.
  • It can range from being a Zero-Shot Meta-Prompting Framework to being a Few-Shot Meta-Prompting Framework, depending on its example requirement.
  • It can range from being a Greedy Meta-Prompting Framework to being a Exploratory Meta-Prompting Framework, depending on its search strategy.
  • ...
• Examples:
  • Core Meta-Prompting Implementations, such as:
    • OPRO (Optimization by Prompting) using performance trajectorys.
    • APE (Automatic Prompt Engineer) with iterative instruction generation.
    • Meta-Prompt Self-Improvement System for recursive optimization.
  • Hybrid Meta-Prompting Systems, such as:
    • Meta-Prompted Evolutionary Framework combining LLM guidance with population methods.
    • Meta-Prompted Reinforcement Learning using LLM reward shaping.
  • Application-Specific Meta-Frameworks, such as:
    • Task-Adaptive Meta-Prompting for domain-specific optimization.
    • Multi-Objective Meta-Prompting for complex constraints.
  • ...
• Counter-Examples:
  • Gradient-Based Prompt Optimization Method, which uses gradient descent rather than LLM reasoning.
  • Manual Prompt Engineering, which requires human designers rather than automated optimization.
  • Random Prompt Search, which lacks intelligent guidance.
  • Fixed Template System, which doesn't self-optimize.
• See: Programmatic Prompt Optimization Framework, LLM-as-Optimizer Technique, Self-Supervised Prompt Optimization (SPO), OPRO Framework, APE Framework, Prompt Evaluation Metric, Optimization Loop, Recursive Optimization, LLM Prompt Optimization Method, Meta-Learning Framework.

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