dspy

Name: dspy
Availability: InStock
Author: Tommy-yw

Program language models declaratively with DSPy for AI systems.

539|39|Updated May 1, 2026

Search Tags:#RAG #AI system development #DSPy #modular AI #language model programming

Ready to run

npx skills add https://github.com/Tommy-yw/RunbookHermes --skill dspy-tommy-yw

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/Tommy-yw/RunbookHermes/tree/main/skills/mlops/research/dspy
Command: npx skills add https://github.com/Tommy-yw/RunbookHermes --skill dspy-tommy-yw

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 enables the construction of complex AI systems using DSPy, a framework for systematic language model programming.

Core Features & Use Cases

Declarative Programming: Program language models declaratively instead of manual prompt engineering.
Automatic Optimization: Optimize prompts and generate modular AI pipelines with data-driven methods.
RAG Systems: Build RAG systems, agents, or classifiers with improved reliability.
Use Case: Develop a multi-hop reasoning system to answer complex questions by integrating document retrieval and reasoning steps.

Quick Start

Run the dspy skill with the following command: dspy predict 'What is the capital of France?'

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 without manual prompt engineering?▼

You can build a RAG system without manual prompt engineering by using DSPy for declarative language model programming. This Skill enables systematic, data-driven automatic optimization to generate modular AI pipelines for reliable document retrieval.

What is the best way to optimize prompts for multi-hop reasoning systems?▼

The best way to optimize prompts for multi-hop reasoning systems is using DSPy's data-driven automatic optimization. This Skill facilitates declarative programming to systematically generate and refine modular AI pipelines instead of relying on manual tuning.

Does this DSPy Skill work with OpenAI and Anthropic language models?▼

Yes, this DSPy Skill works with OpenAI and Anthropic language models. It requires both the DSPy framework and various language model providers as dependencies to implement and execute modular AI systems effectively.

How do I start programming AI systems declaratively with DSPy?▼

You start programming AI systems declaratively with DSPy by running the provided quick start command, such as `dspy predict`, to pass queries. This enables you to shift from manual prompt engineering to systematic language model programming.

Why should I use declarative programming for AI system development instead of manual prompting?▼

You should use declarative programming for AI system development because it replaces manual prompt engineering with systematic, data-driven automatic optimization. This approach allows you to build complex modular AI pipelines, agents, and classifiers with improved reliability.

What are the limitations of using DSPy for language model programming?▼

A limitation of using DSPy for language model programming is its strict dependency on external providers like OpenAI and Anthropic. Complex AI system development also requires advanced internal design reasoning to structure modular pipelines effectively.