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dspy

Compose declarative Language Model pipelines with DSPy modules and optimizers.

Updated Mar 16, 2026
Search Tags:#rag#agents#prompt-optimization#dspy#modular-pipelines#lm-programming#declarative-prompting
One-click install
npx skills add https://github.com/arsity/scholar-tools --skill dspy-arsity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/16-prompt-engineering/dspy
Command: npx skills add https://github.com/arsity/scholar-tools --skill dspy-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy enables building complex AI systems with modular components and declarative LM programming, reducing ad-hoc prompting and boosting maintainability and scalability.

Core Features & Use Cases

  • Declarative LM programming to compose multi-stage pipelines (RAG, agents, classifiers)
  • Optimizers like BootstrapFewShot and MIPRO to improve prompts and performance
  • Strong module composition with typed outputs for safer development and easier maintenance

Quick Start

Configure DSPy to assemble a multi-stage LM workflow that retrieves information, reasons step-by-step, and produces an answer.

Frequently Asked Questions about dspy

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

FAQPage Schema
What is declarative LM programming for modular AI pipelines?▼

Declarative LM programming composes multi-stage pipelines for tasks like RAG and agent orchestration using typed modules, replacing ad-hoc prompting to boost maintainability and scalability.

How do I optimize Language Model prompts automatically?▼

You can optimize prompts automatically using DSPy teleprompters like BootstrapFewShot and MIPRO, which iteratively improve pipeline performance without manual prompt engineering.

Can I use multiple Language Model providers in a modular pipeline?▼

Yes, modular pipelines support multiple LM providers including OpenAI, Anthropic, and Ollama, allowing you to configure and switch models within your declarative workflow.

How do I build a RAG workflow with agent orchestration?▼

You can build RAG workflows with agent orchestration by assembling modular components that retrieve information, reason step-by-step, and produce typed outputs for production deployments.

What is the best way to maintain complex AI systems without ad-hoc prompting?▼

Using declarative LM programming with strong module composition and typed outputs ensures safer development and easier maintenance, eliminating fragile ad-hoc prompting in complex AI systems.

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