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 structured data rather than imperative code, allowing for more systematic and maintainable prompt management.
  • It can typically use Declarative Syntax (such as YAML, JSON, or custom DSLs) to express what the LLM interaction should accomplish 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 and ensure data consistency.
  • It can typically enable Modular Reuse through import mechanisms and include directives for prompt components.
  • It can typically abstract Model-Specific Details, creating a model-agnostic representation of declarative prompt-programming intentions.
  • It can typically support Version Control integration due to its text-based representation.
  • ...
  • It can often include Templating Directives (using systems like Jinja2 or Liquid) for dynamic content generation.
  • It can often implement Control Flow Constructs such as declarative prompt-programming loops, declarative prompt-programming conditionals, and declarative prompt-programming function definitions.
  • It can often provide Type Checking for declarative prompt-programming variables to ensure data integrity.
  • It can often support Model Chaining to create multi-step declarative prompt-programming workflows.
  • It can often enable Collaborative Prompt Development through its human-readable format and defined structure.
  • It can often facilitate Prompt Versioning to track declarative prompt-programming evolution.
  • ...
  • 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 complexity.
  • 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 range from being a Lightweight Declarative Prompt-Programming Framework to being a Comprehensive Declarative Prompt-Programming Framework, depending on its declarative prompt-programming integration capability.
  • It can range from being a Specialized Declarative Prompt-Programming Framework to being a Universal Declarative Prompt-Programming Framework, depending on its declarative prompt-programming model compatibility.
  • ...
  • 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.
  • It can interface with Version Control Systems for declarative prompt-programming history tracking.
  • It can incorporate External Knowledge Sources via declarative prompt-programming retrieval mechanisms.
  • It can work with Validation Tools for declarative prompt-programming quality assurance.
  • ...
• Examples:
  • Declarative Prompt-Programming Framework Implementations, such as:
    • YAML-Based Declarative Prompt-Programming Frameworks, such as:
      • IBM PDL (Prompt Declaration Language) using YAML with Jinja2 for templating functions and type checking via JSON Schema.
      • PromptLang developed by Netflix for content recommendation prompt systems.
      • Pathway Templates for declarative prompt-programming structured workflows.
    • JSON-Based Declarative Prompt-Programming Frameworks, such as:
      • OpenAI Prompt JSON for structured dialogue systems.
      • Prompt Schema for declarative prompt-programming standardization.
    • DSL-Based Declarative Prompt-Programming Frameworks, such as:
      • Impromptu for model-driven declarative prompt-programming with platform independence.
      • Questo for declarative query generation.
  • 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.
    • Code Generation Declarative Prompt-Programming Frameworks for structured programming tasks.
    • Chain-of-Thought Declarative Prompt-Programming Frameworks for multi-step reasoning processes.
    • ReAct Agent Declarative Prompt-Programming Frameworks for reasoning and acting cycles.
  • Declarative Prompt-Programming Framework Capability Implementations, such as:
    • Templating Integration Declarative Prompt-Programming Frameworks, such as:
      • Jinja2-Powered Declarative Prompt-Programming Frameworks for dynamic text substitution.
      • Liquid-Based Declarative Prompt-Programming Frameworks for conditional content inclusion.
    • Control Flow Declarative Prompt-Programming Frameworks, such as:
      • Conditional Logic Declarative Prompt-Programming Frameworks for branching prompt paths.
      • Loop-Enabled Declarative Prompt-Programming Frameworks for iterative prompt processing.
  • ...
• Counter-Examples:
  • Prompt-Optimization Frameworks, such as DSPy and TextGrad, which focus on prompt tuning and automation rather than declarative structure and human readability.
  • Imperative Prompt-Programming Frameworks, which use procedural code to directly define the execution flow of LLM interactions with emphasis on how rather than what.
  • Ad-hoc Prompt Design Approaches, which lack formal structure, schema validation, and reusable components that characterize declarative prompt-programming frameworks.
  • Template-Only Systems, which provide basic text substitution without the full declarative prompt-programming capabilities like schema validation and control flow.
  • Model-Specific Frameworks that tightly couple prompt design to specific LLM implementations, lacking the model-agnostic abstraction of declarative prompt-programming frameworks.
• See: Prompt Engineering, LLM Orchestration Framework, Declarative Programming, YAML Configuration, JSON Schema, Domain-Specific Language, Model-Driven Engineering, Structured Output Generation, Retrieval-Augmented Generation, Chain-of-Thought Prompting.

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