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dspy

Build declarative LLM programs and RAG pipelines with DSPy.

1|Updated Apr 10, 2026
Search Tags:#prompt optimization#rag#agents#structured output#dspy#teleprompters#llm programming
One-click install
npx skills add https://github.com/zerwiz/wayofpi --skill dspy-zerwiz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/zerwiz/wayofpi/tree/main/.hermes/hermes-agent/skills/mlops/research/dspy
Command: npx skills add https://github.com/zerwiz/wayofpi --skill dspy-zerwiz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you replace brittle, manually tuned prompts with maintainable, data-driven language model programs that are easier to debug, optimize, and reuse.

Core Features & Use Cases

  • Declarative signatures and reusable modules for prediction, chain-of-thought reasoning, program-of-thought math, and ReAct-style tool use.
  • Built-in optimization workflows such as BootstrapFewShot, MIPRO, BootstrapFinetune, and KNNFewShot for improving prompts from examples.
  • Production patterns for RAG, agents, classifiers, structured extraction, and multi-stage pipelines with validation and retries.
  • Example use case: create a customer support bot that routes requests, retrieves documentation, and improves itself from labeled interactions.

Quick Start

Use the dspy skill to design a simple question-answering module from your examples and optimize it with a few-shot metric.

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I optimize LLM prompts automatically using examples instead of manual tuning?▼

You can optimize LLM prompts automatically by using optimization teleprompters like BootstrapFewShot and MIPRO to generate and refine prompts from labeled examples, replacing brittle manual tuning with data-driven improvement.

What is the best way to build a RAG pipeline with structured output and validation?▼

The best way to build a RAG pipeline with structured output is using declarative LLM programming to define retrieval workflows and multi-stage systems with built-in validation and retries for production-ready reliability.

How do I create self-improving AI agents that use tools and retrieve documentation?▼

Create self-improving AI agents by defining ReAct-style tool use modules and retrieval workflows, then applying optimization teleprompters to improve agent routing and responses from labeled interactions.

Can I use declarative signatures for multi-stage reasoning and classification tasks?▼

Yes, declarative signatures support multi-stage reasoning, classification, and structured extraction tasks by allowing you to build modular, reusable components for chain-of-thought and program-of-thought workflows.

Does DSPy work for replacing manually tuned prompts in customer support bots?▼

DSPy works for customer support bots by enabling you to route requests, retrieve documentation, and build pipelines that improve themselves from labeled interactions using validation-driven prompt optimization.

What are the limitations of using teleprompters for language model programming?▼

Teleprompters require labeled examples and defined metrics to optimize language model programs, meaning their effectiveness depends on the quality of your training data and the accuracy of your validation criteria.

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