Declarative Prompt-Programming Framework

A Declarative Prompt-Programming Framework is a prompt-programming framework that uses a declarative syntax to express the structure, inputs, outputs, and constraints of LLM interactions while hiding implementation details.

• Context:
  • It can typically represent Prompt Design as data rather than code, allowing for more structured and maintainable prompt management.
  • It can typically use Declarative Syntax (such as YAML, JSON, or custom DSLs) to express what the LLM interaction should look like without specifying how it is executed.
  • It can typically provide Prompt Specification documents that are parsed and executed by a runtime interpreter.
  • It can typically support Schema Validation for both input and output to reduce prompt failures.
  • It can typically enable Modular Reuse through import and include directives for prompt blocks.
  • ...
  • It can often include Templating Directives (using systems like Jinja2 or Liquid) for dynamic text generation.
  • It can often implement Control Flow Constructs such as loops, conditionals, and function definitions.
  • It can often support Version Control integration due to its human-readable format.
  • ...
  • It can range from being a Simple Declarative Prompt-Programming Framework to being a Complex Declarative Prompt-Programming Framework, depending on its declarative prompt-programming feature set.
  • It can range from being a Domain-Specific Declarative Prompt-Programming Framework to being a General-Purpose Declarative Prompt-Programming Framework, depending on its declarative prompt-programming application scope.
  • ...
  • It can integrate with Retrieval-Augmented Generation Pipelines for structured document processing.
  • It can connect to LLM API for standardized model interaction.
  • It can support Agent Orchestration Systems for multi-step workflow management.
  • ...
• Examples:
  • Declarative Prompt-Programming Framework Implementations, such as:
    • YAML-Based Declarative Prompt-Programming Frameworks, such as:
      • PDL (Prompt Declaration Language) using YAML with Jinja2 for templating functions.
      • PromptLang developed by Netflix for content recommendation prompt systems.
    • JSON-Based Declarative Prompt-Programming Frameworks, such as:
      • OpenAI Prompt JSON for structured dialogue systems.
  • Declarative Prompt-Programming Framework Application Domains, such as:
    • Controlled Chatbot Declarative Prompt-Programming Frameworks for regulated conversation flow.
    • RAG Pipeline Declarative Prompt-Programming Frameworks for document retrieval workflow.
    • Regulated Domain Declarative Prompt-Programming Frameworks for finance and legal applications requiring auditability.
  • ...
• Counter-Examples:
  • Prompt-Optimization Frameworks, such as DSPy and TextGrad, which focus on prompt tuning and automation rather than declarative structure.
  • Imperative Prompt-Programming Frameworks, which use procedural code to directly define the execution flow of LLM interactions.
  • Ad-hoc Prompt Design Approaches, which lack formal structure and schema validation.
• See: Prompt Engineering, LLM Orchestration Framework, Declarative Programming, YAML Configuration, JSON Schema.

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