dspy

Compiles declarative language model calls into self-improving AI pipelines.

1|Updated May 21, 2026
One-click install
npx skills add https://github.com/blueskies1818/hermesALIone --skill dspy-blueskies1818
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/blueskies1818/hermesALIone/tree/main/Agent/skills/mlops/research/dspy
Command: npx skills add https://github.com/blueskies1818/hermesALIone --skill dspy-blueskies1818

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves complex AI workflows by providing a framework for declarative language model programming, which automates the creation of AI applications without the need for extensive manual coding.

Core Features & Use Cases

  • Declarative Programming: Write AI logic using simple, declarative statements instead of manual prompt engineering.
  • Modular AI: Build AI components that can be reused and combined into complex workflows.
  • Prompt Optimization: Use data-driven methods to optimize prompts for better AI performance.
  • Use Case: Build a RAG system for a customer support bot, allowing the bot to retrieve information from a knowledge base and generate answers.

Quick Start

Run the dspy skill to build a question-answering module that can understand natural language queries and provide relevant information from a provided dataset.

Frequently Asked Questions about dspy

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

FAQPage Schema
What is declarative language model programming and how does it automate AI workflows?▼

Declarative language model programming compiles declarative statements into self-improving AI pipelines, automating complex tasks like reasoning and data retrieval without manual prompt engineering.

How do I build a RAG system for a customer support bot without manual prompt engineering?▼

You can build a RAG system by writing declarative AI logic that retrieves information from a knowledge base and generates answers, using modular design and few-shot examples for natural language processing.

Do I need OpenAI or Anthropic to use declarative AI pipelines?▼

You need the dspy library to compile declarative AI pipelines, and can optionally use providers like OpenAI, Anthropic Claude, or local models to handle the language model calls.

Can I optimize prompts for language models using data-driven techniques?▼

Yes, declarative language model programming utilizes data-driven optimization techniques and few-shot examples to optimize prompts for better AI performance in complex reasoning workflows.

What is the best way to create modular AI components for complex workflows?▼

The best way to create modular AI components is using declarative programming, which allows you to build reusable modules that combine into self-improving AI pipelines for complex reasoning tasks.