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dspy

Build modular AI pipelines with declarative LM programming and automatic prompt optimization.

Updated Apr 1, 2026
Search Tags:#rag#agents#declarative-programming#prompt-optimization#dspy#ai-pipelines
One-click install
npx skills add https://github.com/founderphantom/zola-agent --skill dspy-founderphantom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/founderphantom/zola-agent/tree/main/skills/mlops/research/dspy
Command: npx skills add https://github.com/founderphantom/zola-agent --skill dspy-founderphantom

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

DSPy provides a cohesive framework to build and maintain complex AI systems by marrying declarative language-model programming with automated prompt optimization and modular components.

Core Features & Use Cases

  • Declarative LM programming: define tasks, data flow, and constraints using signatures and modules.
  • Prompt optimization: automatically improve prompts and few-shot demonstrations with teleprompters.
  • Modular architectures: assemble RAG systems, agents, and classifiers as reusable components.
  • Use cases: build end-to-end QA pipelines, multi-agent workflows, and cross-LM deployments.

Quick Start

Create a simple Predict module and run a basic example to see the result.

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I automatically optimize prompts for AI pipelines?▼

You can optimize prompts for AI pipelines by defining tasks with declarative signatures and using teleprompters to automatically refine prompts and few-shot demonstrations without manual tuning.

What is declarative language model programming?▼

Declarative language model programming is a method where you define tasks, data flow, and constraints using signatures and modules to assemble complex AI systems as reusable components.

How do I build a RAG system as a modular component?▼

You build a RAG system by assembling modular architectures using declarative programming, allowing you to create reusable components for end-to-end QA pipelines and multi-agent workflows.

Can I use declarative LM programming for multi-agent workflows in Python?▼

Yes, declarative LM programming supports multi-agent workflows in Python 3.x environments, enabling you to assemble classifiers and agents as reusable components for cross-LM deployments.

What's the best way to maintain complex AI systems without manual prompt tuning?▼

The best way to maintain complex AI systems without manual prompt tuning is using a cohesive framework that marries declarative programming with automated prompt optimization and modular components.

Do I need Python 3.x to configure modular AI pipelines?▼

Yes, Python 3.x is required to configure modules, evaluate metrics, and deploy modular AI pipelines using the DSPy library and optional optimization modules.

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