Self-Supervised Prompt Optimization (SPO)

A Self-Supervised Prompt Optimization (SPO) is a prompt engineering technique that can be used to create automated prompt improvement systems (that support prompt refinement tasks).

• Context:
  • It can automatically improve Prompt Quality through self-supervised feedback loops.
  • It can typically generate Candidate Prompt Variations using self-supervised prompt mutations.
  • It can typically evaluate Prompt Performance using self-supervised evaluation metrics.
  • It can typically select Optimal Prompt based on self-supervised performance comparisons.
  • It can often implement Self-Consistency Checking through self-supervised output verification.
  • It can often reduce Manual Prompt Engineering Effort through self-supervised iterations.
  • It can range from being a Simple Self-Supervised Prompt Optimization to being a Complex Self-Supervised Prompt Optimization, depending on its self-supervised prompt optimization complexity.
  • It can range from being a Task-Specific Self-Supervised Prompt Optimization to being a General-Purpose Self-Supervised Prompt Optimization, depending on its self-supervised prompt optimization domain scope.
  • ...
• Examples:
  • Self-Supervised Prompt Optimization Methodologies, such as:
    • Reinforcement Learning-Based Self-Supervised Prompt Optimizations, such as:
      • Policy Gradient Self-Supervised Prompt Optimization for self-supervised prompt reward maximization.
      • Q-Learning Self-Supervised Prompt Optimization for self-supervised prompt state-action optimization.
    • Evolutionary Self-Supervised Prompt Optimizations, such as:
      • Genetic Algorithm Self-Supervised Prompt Optimization for self-supervised prompt population evolution.
      • Simulated Annealing Self-Supervised Prompt Optimization for self-supervised prompt space exploration.
  • Self-Supervised Prompt Optimization Applications, such as:
    • Task-Specific Self-Supervised Prompt Optimizations, such as:
      • Code Generation Self-Supervised Prompt Optimization for self-supervised code prompt refinement.
      • Question Answering Self-Supervised Prompt Optimization for self-supervised QA prompt enhancement.
    • Model-Specific Self-Supervised Prompt Optimizations, such as:
      • LLM Self-Supervised Prompt Optimization for self-supervised large language model prompt tuning.
      • Multimodal Self-Supervised Prompt Optimization for self-supervised cross-modal prompt alignment.
  • ...
• Counter-Examples:
  • Supervised Prompt Optimization, which requires human-labeled prompt quality assessment rather than self-supervised feedback.
  • Manual Prompt Engineering, which relies on human expertise rather than self-supervised automation.
  • Prompt Template Library, which provides static prompt collections rather than self-supervised prompt evolution.
• See: Prompt Engineering, Self-Supervised Learning, Reinforcement Learning from AI Feedback, Automatic Prompt Optimization, LLM Prompt Tuning.

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