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dspy

Automate AI system creation with declarative language model programming.

1|1|Updated May 9, 2026
Search Tags:#prompt optimization#RAG#declarative programming#AI system#language model
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill dspy-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/skills/mlops/research/dspy
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill dspy-ldzhhxx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dspy, openai, anthropic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps you build complex AI systems with declarative language model programming, auto-optimize prompts, and create modular AI pipelines.

Core Features & Use Cases

  • Declarative LM Programming: Program language models declaratively instead of manual prompt engineering.
  • Prompt Optimization: Automatically optimize prompts using data-driven methods.
  • Modular AI Pipelines: Create maintainable and portable AI pipelines.
  • Use Case: Build a RAG system, agent, or classifier with better reliability and systematic improvement.

Quick Start

Use the dspy skill to create a question answering module for your AI system.

Frequently Asked Questions about dspy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG system using declarative programming instead of manual prompt engineering?▼

Declarative programming builds a RAG system by defining language model behavior programmatically. This approach replaces manual prompt engineering with modular AI pipelines, enabling systematic prompt optimization and reliable AI system development.

What is the best way to auto-optimize prompts for complex AI pipelines?▼

Auto-optimize prompts by applying data-driven methods to declarative language model programs. This technique systematically improves modular AI pipelines, ensuring better reliability for complex AI systems without manual prompt tuning.

Can I use OpenAI and Anthropic models with declarative LM programming frameworks?▼

Declarative LM programming supports OpenAI and Anthropic integration for language model optimization. You can configure these dependencies to power complex AI systems, build modular pipelines, and automate prompt optimization.

Does declarative language model programming work for building AI agents and classifiers?▼

Declarative language model programming effectively builds AI agents, classifiers, and RAG systems. It shifts development from manual prompt engineering to systematic modular pipeline construction, yielding maintainable and portable AI systems.

Why should I use declarative programming for AI system development over manual prompt engineering?▼

Declarative programming structures AI system development into modular, maintainable pipelines. Unlike manual prompt engineering, it enables automated, data-driven prompt optimization, providing systematic reliability and portability for complex language model workflows.

What are the limitations of using declarative programming for complex AI systems?▼

Declarative programming for complex AI systems requires installing specific dependencies like dspy, openai, and anthropic. While it streamlines prompt optimization and pipeline construction, users must manage these integrations to ensure modular AI systems function correctly.

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