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dspy

Automate language-model pipeline construction with declarative signatures and modules.

Updated Apr 3, 2026
Search Tags:#modularity#declarative#prompts#dspy#language-models#ai-pipelines
One-click install
npx skills add https://github.com/handsomelong922/my-codex-skills --skill dspy-handsomelong922
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/dspy
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill dspy-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

DSPy provides a declarative, modular framework to design, compose, and optimize complex language-model workflows without ad-hoc prompt engineering.

Core Features & Use Cases

  • Declarative Signatures and Modules for building AI pipelines
  • Built-in optimizers (teleprompters) to improve prompts and performance
  • Supports retrieval-augmented generation, ReAct agents, and multi-stage pipelines
  • Production-grade tooling for experimentation, evaluation, and deployment

Quick Start

Install DSPy and create a simple QA module, then run it to obtain a structured answer.

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I build modular AI pipelines with declarative prompts?▼

You can build modular AI pipelines by defining declarative signatures and modules, which allows you to compose multi-stage language-model workflows without relying on ad-hoc prompt engineering.

What is the best way to optimize language-model workflows for production?▼

Optimizing language-model workflows for production involves using built-in optimizers, or teleprompters, to automatically improve prompt performance and support telemetry-enabled evaluation.

Can I use declarative modules to create retrieval-augmented generation and ReAct agents?▼

Yes, declarative modules support the construction of retrieval-augmented generation workflows, ReAct agents, and multi-stage systems designed for production-ready deployments.

Does this framework work with OpenAI and Anthropic language models?▼

Yes, the framework supports integration with OpenAI and Anthropic language models, enabling deterministic execution and telemetry-enabled evaluation across different providers.

Why should I use a declarative framework instead of ad-hoc prompt engineering?▼

A declarative framework eliminates the instability of ad-hoc prompt engineering by defining signatures and modules, ensuring deterministic execution and built-in optimization for complex language-model pipelines.

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