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dspy

Automate prompt generation and optimization for language models with DSPy.

Updated Jun 17, 2026
Search Tags:#prompt engineering#AI development#RAG#declarative programming#language models
One-click install
npx skills add https://github.com/Brilly-Bohyun/skill-repository --skill dspy-brilly-bohyun
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/Brilly-Bohyun/skill-repository/tree/main/mlops/research/dspy
Command: npx skills add https://github.com/Brilly-Bohyun/skill-repository --skill dspy-brilly-bohyun

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 streamlines AI-powered language model programming, enabling automatic prompt optimization and building retrieval-augmented generation (RAG) systems with ease.

Core Features & Use Cases

  • Declarative LM Programming: Eliminate manual prompt engineering, build complex AI systems declaratively.
  • Prompt Optimization: Auto-optimize prompts using data-driven methods for better model outputs.
  • RAG Systems: Build RAG systems, agents, or classifiers with improved reliability.
  • Use Case: Use this Skill to create a modular AI pipeline that automatically answers questions based on relevant documents retrieved from a knowledge base.

Quick Start

Use the dspy skill to build a question answering module for your chatbot.

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I automate prompt optimization for language models?▼

You can automate prompt optimization by using declarative programming to build modular AI pipelines. This approach applies data-driven methods to auto-optimize prompts, eliminating manual engineering and improving model output quality.

How do I build a retrieval-augmented generation system for document question answering?▼

Building a retrieval-augmented generation (RAG) system involves creating a modular pipeline that retrieves relevant documents from a knowledge base. This declarative approach processes retrieved text sources to automatically answer questions with improved reliability.

Do I need OpenAI or Anthropic to use declarative LM programming?▼

Yes, declarative LM programming requires specific language model providers such as OpenAI or Anthropic. You must configure these dependencies alongside DPyTorch to execute automated prompt generation and AI pipeline processing.

What is the best way to eliminate manual prompt engineering for complex AI pipelines?▼

The best way to eliminate manual prompt engineering is adopting a declarative programming approach for language models. This method automatically generates and optimizes prompts, allowing you to build complex AI pipelines and classifiers declaratively.

Can I use declarative programming to build AI agents and classifiers?▼

Yes, declarative programming supports building AI agents and classifiers by streamlining language model programming. It creates modular AI pipelines that retrieve and process information from text sources with improved system reliability.

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