AI Prompt Design Pattern

An AI Prompt Design Pattern is a design pattern that provides a reusable solution for effectively communicating with artificial intelligence systems to achieve specific outcomes.

• Context:
  • It can typically structure AI Prompt Instruction with AI prompt components, AI prompt sections, and AI prompt formatting conventions.
  • It can typically improve AI Response Quality through AI prompt clarity, AI prompt specificity, and AI prompt contextual information.
  • It can typically optimize AI Task Completion by incorporating AI prompt goal statements, AI prompt constraints, and AI prompt success criteria.
  • It can typically enhance AI Reasoning Process through AI prompt thinking steps, AI prompt self-check mechanisms, and AI prompt reflection techniques.
  • It can typically guide AI Output Format using AI prompt template structures, AI prompt example outputs, and AI prompt formatting requirements.
  • ...
  • It can often incorporate AI Few-Shot Learning through AI prompt demonstration examples, AI prompt input-output pairs, and AI prompt pattern illustrations.
  • It can often address AI Limitation by including AI prompt fallback instructions, AI prompt error handling guidance, and AI prompt boundary recognition.
  • It can often facilitate AI Collaboration through AI prompt role definitions, AI prompt interaction protocols, and AI prompt feedback mechanisms.
  • It can often enable AI Creativity with AI prompt exploration parameters, AI prompt novelty incentives, and AI prompt divergent thinking triggers.
  • ...
  • It can range from being a Simple AI Prompt Design Pattern to being a Complex AI Prompt Design Pattern, depending on its AI prompt component complexity.
  • It can range from being a Task-Specific AI Prompt Design Pattern to being a General-Purpose AI Prompt Design Pattern, depending on its AI prompt application scope.
  • It can range from being a Rigid AI Prompt Design Pattern to being a Flexible AI Prompt Design Pattern, depending on its AI prompt adaptation capability.
  • ...
  • It can have AI Prompt Efficiency for AI prompt token optimization.
  • It can provide AI Prompt Transferability across AI prompt different models.
  • It can support AI Prompt Modularity for AI prompt component reuse.
  • ...
• Examples:
  • Instructional AI Prompt Design Patterns, such as:
    • Direct Instruction AI Prompt Design Patterns, such as:
      • Step-by-Step AI Prompt Design Pattern for AI prompt procedural task.
      • Expert Role AI Prompt Design Pattern for AI prompt specialized knowledge.
    • Meta-Instructional AI Prompt Design Patterns, such as:
      • Self-Reflection AI Prompt Design Pattern for AI prompt error reduction.
      • Chain-of-Thought AI Prompt Design Pattern for AI prompt reasoning transparency.
  • Structural AI Prompt Design Patterns, such as:
    • Format-Based AI Prompt Design Patterns, such as:
      • JSON AI Prompt Design Pattern for AI prompt structured data extraction.
      • Markdown AI Prompt Design Pattern for AI prompt content organization.
    • Template-Based AI Prompt Design Patterns, such as:
      • Fill-in-the-Blank AI Prompt Design Pattern for AI prompt consistent output.
      • Sectioned Document AI Prompt Design Pattern for AI prompt complex content generation.
  • Interaction AI Prompt Design Patterns, such as:
    • Dialogue-Based AI Prompt Design Patterns, such as:
      • Socratic AI Prompt Design Pattern for AI prompt guided discovery.
      • Debate AI Prompt Design Pattern for AI prompt multiple perspective.
  • ...
• Counter-Examples:
  • Unstructured Querys, which lack AI prompt systematic organization and AI prompt clear objective.
  • Vague Instructions, which omit AI prompt specific guidance and AI prompt success criteria.
  • Contradictory Requirements, which contain AI prompt conflicting constraints and AI prompt incompatible goals.
• See: Prompt Engineering Technique, AI Interaction Design, Natural Language Instruction, Structured Prompt Format, AI Response Pattern.

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