Prompt-Programming Framework

A Prompt-Programming Framework is a AI programming framework that provides structured approaches for designing, optimizing, or orchestrating prompt interactions with large language models.

• Context:
  • It can typically standardize Prompt-Programming Workflows through prompt-programming abstraction layers.
  • It can typically reduce Prompt-Programming Complexity through prompt-programming patterns and prompt-programming best practices.
  • It can typically improve Prompt-Programming Reliability by enforcing prompt-programming structure and prompt-programming validation.
  • It can typically support Prompt-Programming Reusability through prompt-programming components and prompt-programming templates.
  • It can typically facilitate Prompt-Programming Maintenance via prompt-programming version control and prompt-programming documentation.
  • ...
  • It can often enable Prompt-Programming Evaluation through prompt-programming metrics and prompt-programming benchmarks.
  • It can often provide Prompt-Programming Debugging capabilities via prompt-programming trace and prompt-programming visualization.
  • It can often include Prompt-Programming Integration with external prompt-programming tools and prompt-programming services.
  • ...
  • It can range from being a Simple Prompt-Programming Framework to being a Complex Prompt-Programming Framework, depending on its prompt-programming feature complexity.
  • It can range from being a Domain-Specific Prompt-Programming Framework to being a General-Purpose Prompt-Programming Framework, depending on its prompt-programming application scope.
  • It can range from being a Low-Level Prompt-Programming Framework to being a High-Level Prompt-Programming Framework, depending on its prompt-programming abstraction level.
  • ...
  • It can support LLM Application Development through prompt-programming productivity enhancement.
  • It can enable AI System Engineering through prompt-programming quality assurance.
  • It can facilitate Production LLM Deployment through prompt-programming operational standardization.
  • ...
• Examples:
  • Prompt-Programming Framework Types, such as:
    • Declarative Prompt-Programming Frameworks, which treat prompt-programming design as data rather than code, using formats like YAML or JSON.
    • Prompt-Optimization Frameworks, which search, tune, or learn better prompt-programming patterns through automated methods.
    • LLM Pipeline Frameworks, which chain multiple prompt-programming components, tool invocations, and state transitions.
    • Agent Orchestration Frameworks, which coordinate autonomous agents through prompt-programming workflows.
  • Prompt-Programming Framework Implementations, such as:
    • PDL for declarative prompt-programming with YAML and Jinja2.
    • DSPy for gradient-free demonstration search in prompt-programming optimization.
    • TextGrad for LLM-generated textual gradients in prompt-programming improvement.
    • LangGraph for prompt-programming directed graph execution.
    • CrewAI for multi-agent prompt-programming orchestration.
    • AutoGen for flexible prompt-programming agent development.
  • ...
• Counter-Examples:
  • Ad-hoc Prompt Engineering Approaches, which lack prompt-programming formalization and prompt-programming systematic methodology.
  • Basic LLM API Wrappers, which provide simple API access without prompt-programming abstraction or prompt-programming structure.
  • Raw Text Template Systems, which offer basic string interpolation without prompt-programming validation or prompt-programming optimization.
• See: Prompt Engineering, LLM System Architecture, Software Framework, Agent Framework, Natural Language Processing Tool.

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