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dspy

Compile declarative code into self-improving pipelines for language model applications.

1|Updated Jun 23, 2026
Search Tags:#prompt optimization#AI development#RAG#declarative programming#LM programming
One-click install
npx skills add https://github.com/whichguy/hermes-skills-marketplace --skill dspy-whichguy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/whichguy/hermes-skills-marketplace/tree/main/skills/dspy
Command: npx skills add https://github.com/whichguy/hermes-skills-marketplace --skill dspy-whichguy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The dspy skill automates and optimizes the development of AI-driven language model applications, streamlining the process of building complex AI systems and improving the reliability of AI outputs.

Core Features & Use Cases

  • Declarative Programming: Simplifies LM programming by allowing developers to specify tasks declaratively, rather than manually crafting prompts.
  • Prompt Optimization: Automatically optimizes prompts using data-driven methods, resulting in more accurate and reliable AI outputs.
  • RAG Systems: Enables the creation of Retrieval-Augmented Generation systems, improving the performance of AI agents and classifiers.
  • Use Case: Imagine you're developing a customer support bot that needs to answer questions about products. dspy allows you to define the bot's responses in a modular, maintainable way, with optimized prompts to ensure accurate and helpful answers.

Quick Start

Use the dspy skill to define a signature for a QA task and train it on your dataset.

Frequently Asked Questions about dspy

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

FAQPage Schema
How does declarative programming improve language model application development?▼

Declarative programming simplifies language model application development by allowing developers to specify tasks modularly, compiling declarative code into self-improving pipelines instead of manually crafting prompts.

How do I automatically optimize prompts for AI-driven Q&A systems?▼

You can automatically optimize prompts for AI-driven Q&A systems by using data-driven methods to compile declarative code into self-improving pipelines, ensuring more accurate and reliable AI outputs.

Can I use dspy to build Retrieval-Augmented Generation (RAG) systems?▼

Yes, you can use dspy to build Retrieval-Augmented Generation systems, which enhances the performance of AI agents and classifiers by leveraging advanced optimization techniques and modular design.

Does declarative LM programming require manually writing prompts for multi-step reasoning?▼

No, declarative LM programming eliminates manual prompt writing for multi-step reasoning by compiling declarative code into optimized, self-improving pipelines for complex NLP tasks.

What is the best way to structure a customer support bot for accurate product answers?▼

The best way to structure a customer support bot is by defining responses declaratively in a modular, maintainable way, utilizing prompt optimization to ensure accurate and helpful answers.

When should I avoid manual prompt crafting for NLP classification tasks?▼

You should avoid manual prompt crafting for NLP classification tasks when you need reliable outputs, as declarative programming compiles code into self-improving pipelines using data-driven optimization.

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