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 enforce Output Structure through schema definitions, constraints, or type systems.
  • ...
  • 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 Multi-Step Workflows by declarative prompt-programming chaining or declarative prompt-programming orchestration.
  • 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 often include Error Handling Mechanisms that manage LLM output failures through retry logic and fallback strategy.
  • It can often provide Validation Feedback Loops where invalid outputs trigger corrective reprompts.
  • ...
  • 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 range from being a Single-Call Declarative Prompt-Programming Framework to being a Multi-Step Declarative Prompt-Programming Framework, depending on its declarative prompt-programming orchestration complexity.
  • It can range from being a Code-Embedded Declarative Prompt-Programming Framework to being a Standalone Declarative Prompt-Programming Framework, depending on its declarative prompt-programming host language relationship.
  • ...
  • 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.
  • It can integrate with CI/CD Pipelines for automated prompt testing and prompt deployment.
  • It can connect to Output Validation Systems for LLM output reliability enhancement.
  • It can support Safe Guardrail Integration for LLM response filtering and harmful content prevention.
  • ...
• Examples:
  • Declarative Prompt-Programming Framework Syntax Approaches, such as:
    • YAML-Based Declarative Prompt-Programming Frameworks, such as:
      • IBM PDL (Prompt Declaration Language) using YAML with Jinja2 for templating functions, type checking via JSON Schema, and control flow constructs like conditionals and loops.
      • Azure Prompt Flow representing multi-step workflows as directed acyclic graphs with step definitions and data flows.
      • 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:
      • LMQL (Language Model Query Language) using constraint expressions with where clauses to enforce output patterns.
      • BAML (Basically a Made-up Language) implementing prompts as functions with parameter types and return types.
      • Colang in NVIDIA NeMo Guardrails for conversational AI safety and dialogue flow management.
      • Impromptu for model-driven declarative prompt-programming with platform independence.
      • Questo for declarative query generation.
    • Code-Embedded Declarative Prompt-Programming Frameworks, such as:
      • Microsoft Guidance with its inline control directives for stepwise generation control.
      • DSPy providing declarative modules and signatures for LLM-powered components.
      • Marvin AI Functions using Python decorators and type annotations for LLM function specification.
  • Declarative Prompt-Programming Framework Complexity Levels, such as:
    • Comprehensive Declarative Prompt-Programming Frameworks, such as:
      • IBM PDL (Prompt Declaration Language) supporting full control flow, type checking, model chaining, and function definitions.
      • Azure Prompt Flow with enterprise features for LLMOps, experiment tracking, and deployment management.
      • DSPy offering teleprompters for prompt optimization and advanced module composition.
    • Moderate Declarative Prompt-Programming Frameworks, such as:
      • BAML balancing schema validation with approachable syntax using function metaphors.
      • LMQL focusing on constrained generation and output filtering.
      • NeMo Guardrails specializing in conversational safety rules and dialogue policy.
    • Lightweight Declarative Prompt-Programming Frameworks, such as:
      • Marvin AI Functions emphasizing developer simplicity through familiar function syntax.
      • Guardrails (RAIL) focusing on output validation and schema conformance.
  • Declarative Prompt-Programming Framework Application Domains, such as:
    • General-Purpose Declarative Prompt-Programming Frameworks for cross-domain applications, such as:
      • IBM PDL supporting universal usage across domains.
      • DSPy enabling self-optimizing pipelines for diverse tasks.
    • Specialized Domain Declarative Prompt-Programming Frameworks, such as:
      • Guardrails (RAIL) for output validation and schema enforcement.
      • NeMo Guardrails for conversational AI safety and topic management.
      • 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 Feature Implementations, such as:
    • Schema Validation Declarative Prompt-Programming Frameworks, such as:
      • Guardrails (RAIL) with RAIL specifications for output validation and automated retries.
      • BAML using type-driven prompts and schema-based parsing.
    • Control Flow Declarative Prompt-Programming Frameworks, such as:
      • IBM PDL with declarative loops, conditional branches, and function calls.
      • Microsoft Guidance using inline directives for generation control.
    • Orchestration-Focused Declarative Prompt-Programming Frameworks, such as:
      • Azure Prompt Flow with DAG-based workflows and step sequencing.
      • DSPy Modules for composable pipelines and optimized chains.
  • ...
• Counter-Examples:
  • Prompt-Optimization Frameworks, such as TextGrad and early versions of DSPy, 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.
  • Raw API Interactions where prompt text is directly passed to LLM API without intermediate representation or declarative structure.
  • Hardcoded Prompt Systems that embed prompt strings directly in application code, preventing separation of concerns and prompt versioning.
• 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, LLMOps, Prompt Template, Prompt Management, AI Function, Workflow Orchestration.

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References

2025-05-01

• Perplexity.ai https://chatgpt.com/share/6813e182-f24c-8012-bb23-c79925fa2b72
  • Question: Identify and compare existing declarative prompt-programming frameworks that align with these characteristics—such as using declarative syntax (YAML, JSON, DSLs), supporting modularity, templating, schema validation, model-agnostic abstractions, and collaborative versioning.
  • Answer: Declarative prompt-programming frameworks let developers **define prompt logic as data/configuration** rather than writing imperative code. These systems use formats like YAML, JSON, or domain-specific languages (DSLs) to specify how an LLM should behave, including input/output schemas, control flow, and multi-step interactions.
    • Framework Comparisons:
      • IBM PDL (Prompt Declaration Language):
        • Uses YAML DSL (`.pdl` YAML files) for comprehensive prompt workflows.
        • Features universal usage across domains with modularity, templating, loops, conditionals, functions, and type-checked I/O.
        • Provides model-agnostic support for any LLM via connectors.
      • Stanford DSPy:
        • Implements a Python DSL with declarative modules in code.
        • Emphasizes self-optimizing pipelines and improving prompts over time.
        • Offers modular composition, automated optimization (teleprompters), and chain-of-thought modules.
        • Works with open-source models and commercial APIs like OpenAI.
      • Microsoft Guidance:
        • Features a templating DSL embedded in Python (guidance library).
        • Focuses on single-call orientation for controlling one prompt-response flow.
        • Provides inline control of generation with if/for loops, choice selection, and output formatting (regex guides).
        • Supports various model providers through abstraction.
      • LMQL (Language Model Query Language):
        • Uses a Python-embedded DSL with query language and where clauses.
        • Specializes in constrained generation for enforcing output schemas/patterns.
        • Enables schema validation through where statements and templates with constraint expressions.
        • Works with local HuggingFace models with limited support for external APIs.
      • BAML (Basically a Made-up Language):
        • Implements a custom schema-driven DSL with function definitions in multiple languages.
        • Designed for reliable AI agents & workflows with schema-driven prompts.
        • Treats prompts as typed functions with parameters and return types.
        • Includes Jinja2 templating, full type-safety, retries, and streaming.
        • Provides cross-language integration (Python, JS, Ruby, Go).
      • Guardrails (RAIL by Guardrails AI):
        • Uses RAIL markup (XML/YAML hybrid specification).
        • Focuses on output validation and safety for any prompt.
        • Defines output schema and format requirements in RAIL spec.
        • Automates re-asking the LLM until output meets schema.
        • Offers model-agnostic guards for consistent outputs across models.
      • NVIDIA NeMo Guardrails:
        • Combines YAML config with Colang DSL (policy files).
        • Specializes in conversational AI safety and flow control.
        • Uses rails (rules) for input filtering, response validation, and dialogue flow management.
        • Supports conditional flows, multi-turn dialogues, and integration of custom actions.
        • Integrates with various model APIs through connectors.
      • Azure Prompt Flow:
        • Employs YAML (flow DAG definition).
        • Targets LLMOps and production pipelines.
        • Defines flows as DAGs of steps (LLM calls, Python functions, tools) in YAML.
        • Facilitates variables, data passing between steps, and CI/CD integration.
        • Works with Azure OpenAI, other providers via "connections", and custom tools.
      • Marvin (AI Functions):
        • Implements Python decorators using type hints & docstrings.
        • Takes a function-oriented approach to prompt design.
        • Uses Python type annotations and docstring to declare what the LLM should do.
        • Derives schema from types to ensure output validity.
        • Works with various model APIs through type independence.
  • Citations:

[1] https://www.infoq.com/news/2023/06/guidance-microsoft-language/
[2] https://www.latent.space/p/guaranteed-quality-and-structure
[3] https://www.restack.io/p/promptflow-answer-yaml-flow-sequence-cat-ai
[4] https://www.datacamp.com/blog/dspy-introduction
[5] https://www.twosigma.com/articles/a-guide-to-large-language-model-abstractions/

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