Metaprompting Technique

A Metaprompting Technique is a prompt optimization prompt engineering technique that uses LLM systems to generate and optimize their own LLM prompts.

• AKA: Self-Optimizing Prompt Technique, Prompt-Generating Prompt Method, Meta-Level Prompting.
• Context:
  • It can typically generate Metaprompting Candidate Prompts through metaprompting generation patterns.
  • It can typically evaluate Metaprompting Prompt Quality via metaprompting assessment criteria.
  • It can typically refine Metaprompting Prompt Versions through metaprompting iteration cycles.
  • It can typically optimize Metaprompting Prompt Performance via metaprompting feedback loops.
  • It can typically discover Metaprompting Prompt Patterns through metaprompting analysis methods.
  • ...
  • It can often improve Metaprompting Task Accuracy with metaprompting systematic optimization.
  • It can often reduce Metaprompting Manual Effort through metaprompting automation processes.
  • It can often enhance Metaprompting Prompt Robustness via metaprompting variation testing.
  • It can often accelerate Metaprompting Development Speed through metaprompting rapid iterations.
  • ...
  • It can range from being a Simple Metaprompting Technique to being a Complex Metaprompting Technique, depending on its metaprompting sophistication level.
  • It can range from being a Single-Stage Metaprompting Technique to being a Multi-Stage Metaprompting Technique, depending on its metaprompting iteration depth.
  • It can range from being a Domain-Specific Metaprompting Technique to being a General-Purpose Metaprompting Technique, depending on its metaprompting application scope.
  • It can range from being a Manual Metaprompting Technique to being an Automated Metaprompting Technique, depending on its metaprompting automation degree.
  • ...
  • It can integrate with Self-Reflection Rubrics for metaprompting quality assessment.
  • It can connect to Reasoning Effort Parameters for metaprompting optimization depth.
  • It can interface with Code Editing Rules for metaprompting code generation.
  • It can synchronize with System Prompt Tunings for metaprompting behavior optimization.
  • It can communicate with Verbosity Parameters for metaprompting output control.
  • ...
• Examples:
  • Metaprompting Implementations, such as:
    • GPT-5 Metaprompting System using metaprompting self-improvement loops for metaprompting prompt refinement.
    • AutoPrompt Framework implementing metaprompting gradient-based searches for metaprompting optimal prompts.
    • DSPy Metaprompting Module applying metaprompting programmatic optimization for metaprompting task-specific prompts.
  • Metaprompting Patterns, such as:
    • Iterative Metaprompting Pattern using metaprompting progressive refinements through metaprompting multiple generations.
    • Comparative Metaprompting Pattern generating metaprompting prompt variants for metaprompting A/B testing.
    • Evolutionary Metaprompting Pattern applying metaprompting genetic algorithms for metaprompting prompt evolution.
  • Metaprompting Applications, such as:
    • Task-Specific Metaprompting optimizing metaprompting classification prompts for metaprompting accuracy improvement.
    • Instruction Metaprompting refining metaprompting system instructions for metaprompting behavior tuning.
    • Example Metaprompting generating metaprompting few-shot examples for metaprompting learning enhancement.
  • ...
• Counter-Examples:
  • Manual Prompt Engineering, which relies on human expertise rather than automated optimization.
  • Template-Based Prompting, which uses fixed structures rather than dynamic generation.
  • Random Prompt Search, which lacks systematic optimization and quality assessment.
• See: Prompt Engineering Technique, Self-Supervised Prompt Optimization, LLM Prompt Optimization, Automated Prompt Discovery, Prompt Quality Metric, Recursive Improvement Pattern, AI Self-Improvement.

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