dspy

Name: dspy
Availability: InStock
Author: DifanaDAP

Automates AI system development through declarative, data-driven prompt optimization with DSPy, OpenAI, Anthropic.

Updated Apr 29, 2026

Search Tags:#prompt optimization #AI development #declarative programming #language models #modular AI

Ready to run

npx skills add https://github.com/DifanaDAP/hermes-backup --skill dspy-difanadap

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/DifanaDAP/hermes-backup/tree/main/workspace/skills/mlops/research/dspy
Command: npx skills add https://github.com/DifanaDAP/hermes-backup --skill dspy-difanadap

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?

The dspy Skill solves the problem of complex AI system development by providing a declarative programming approach to LM programming, which automates prompt engineering and improves model outputs with optimization.

Core Features & Use Cases

Declarative Programming: Build complex AI systems with multiple components and workflows using a simple declarative syntax.
Prompt Optimization: Automatically optimize prompts for better model performance.
Modular AI: Create modular AI pipelines that are maintainable and portable.
Use Case: Imagine you need to build an AI system that generates and evaluates mathematical proofs. Use dspy to define the signature for the AI's behavior, configure the language model, and use optimizers to improve the proof generation process.

Quick Start

Use the dspy skill to build a mathematical proof generation system with the following steps:

Define the signature for the AI's behavior.
Configure the language model with the appropriate parameters.
Use optimizers to improve the proof generation process.

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?▼

Automate prompt optimization by using declarative programming to define AI behavior signatures and applying data-driven optimizers to improve language model outputs. This approach replaces manual prompt engineering with automated compilation.

What is declarative programming for AI development?▼

Declarative programming for AI development involves defining the desired behavior signatures and workflows rather than writing explicit prompts. This method automates the configuration and optimization of multi-component language model systems.

How do I build modular AI pipelines with multiple components?▼

Build modular AI pipelines by using declarative syntax to define system components and workflows. This creates maintainable and portable AI architectures that automatically compile and optimize language model interactions.

Does DSPy work with OpenAI and Anthropic models?▼

Yes, DSPy works with OpenAI and Anthropic models. It requires both the openai and anthropic dependencies alongside the dspy library to compile and execute declarative programs with automatic optimization.

What is the best way to structure multi-component AI workflows?▼

The best way to structure multi-component AI workflows is using declarative programming to define behavior signatures, configure language models, and apply optimizers. This modular approach ensures maintainable and portable AI pipelines.

Why does manual prompt engineering fail for complex AI systems?▼

Manual prompt engineering fails for complex AI systems because it lacks scalability and adaptability. Declarative programming solves this by using data-driven optimization to automatically refine prompts and improve language model outputs.