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dspy

Automate modular AI system construction with declarative language model programming.

7|3|Updated Jan 15, 2026
Search Tags:#workflow#rag#agents#declarative#module#prompt-optimization#lm
One-click install
npx skills add https://github.com/Zpankz/mcp-skillset --skill dspy-zpankz
Or copy as Structured Prompt for Agentâ–¼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/Zpankz/mcp-skillset/tree/main/dspy
Command: npx skills add https://github.com/Zpankz/mcp-skillset --skill dspy-zpankz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy provides a declarative framework for programming language models, replacing ad-hoc prompting with structured modules, signatures, and pipelines that are easier to reason about and reuse.

Core Features & Use Cases

  • Declarative signatures and modular composition enable building RAG pipelines, agents, and end-to-end AI applications without writing low level prompts.
  • Built in tools for configuring language models, designing modules, and optimizing prompts across workflows, from data preparation to deployment.
  • Real-world use cases include designing robust AI systems, assembling modular AI pipelines, and enabling systematic prompt optimization at scale.

Quick Start

Configure a language model, define a module, and run it to see DSPy in action.

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I build modular RAG pipelines without writing low-level prompts?â–¼

You can build modular RAG pipelines by defining declarative signatures and composing modules. This replaces ad-hoc prompting with structured workflows that are easier to reason about and reuse for your AI applications.

What is automatic prompt optimization and how does it work for language models?â–¼

Automatic prompt optimization systematically refines language model instructions across workflows. It uses declarative modules to replace manual prompt tuning, enabling scalable and robust AI system performance.

How do I configure language models for declarative AI workflows?â–¼

Configure language models by setting up the environment and defining declarative signatures for your modules. This structured approach allows you to compose end-to-end AI pipelines and agents seamlessly.

Can I assemble modular agents using a declarative framework?â–¼

Yes, you can assemble modular agents by defining declarative signatures and composing them into workflows. This approach eliminates the need for ad-hoc prompting when building complex AI systems.

What is the best way to automate prompt tuning at scale for AI applications?â–¼

Automate prompt tuning at scale by applying a declarative framework with built-in prompt optimization tools. This replaces manual adjustments with systematic module composition across your workflows.

Why should I use declarative modules instead of ad-hoc prompting for AI systems?â–¼

Declarative modules replace ad-hoc prompting with structured signatures and pipelines that are easier to reason about and reuse. This enables systematic prompt optimization and robust modular AI system construction.

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