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dspy

Build modular AI systems with DSPy and optimize prompts automatically.

Updated Apr 2, 2026
Search Tags:#prompt optimization#rag#agents#declarative programming#dspy#teleprompters
One-click install
npx skills add https://github.com/JKhyro/HERMES-AGENT --skill dspy-jkhyro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/JKhyro/HERMES-AGENT/tree/main/skills/mlops/research/dspy
Command: npx skills add https://github.com/JKhyro/HERMES-AGENT --skill dspy-jkhyro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy removes the burden of hand-tuning prompts and brittle multi-step chains by giving you a structured way to build language model applications that can improve from data.

Core Features & Use Cases

  • Declarative LM programming: Define inputs and outputs as signatures instead of writing fragile prompt text by hand.
  • Modular AI systems: Compose reusable modules for prediction, reasoning, retrieval, tool use, and validation.
  • Automatic optimization: Improve performance with teleprompters such as BootstrapFewShot, MIPRO, and BootstrapFinetune.
  • Real-world applications: Build RAG systems, research agents, classifiers, document workflows, and multi-stage pipelines with better reliability.
  • Use case: A team can turn a rough question-answer prototype into a measurable, optimized support or research system without rewriting the whole stack.

Quick Start

Ask the skill to design a DSPy pipeline for your task, including the signatures, module structure, and an optimization strategy for your target use case.

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I optimize language model prompts automatically without manual tuning?▼

You can optimize language model prompts automatically by defining declarative signatures for inputs and outputs, then applying teleprompter optimizers like BootstrapFewShot or MIPRO to validate and improve performance from data.

What is the best way to build a modular RAG pipeline that improves from data?▼

Building a modular RAG pipeline requires composing reusable modules for retrieval and prediction, defining declarative signatures, and using teleprompters to automatically optimize the language model prompts based on your validation metrics.

How do I structure multi-stage reasoning workflows for AI agents?▼

Multi-stage reasoning workflows for AI agents are structured by composing modular components for prediction, tool use, and validation, then defining declarative signatures to pass data between stages without writing fragile prompt text.

Can I use declarative programming to replace hand-tuned prompts for classifiers?▼

Declarative programming replaces hand-tuned prompts for classifiers by having you define input and output signatures instead of prompt text, allowing automatic optimizers to validate and improve the classification logic systematically.

What are the limitations of using teleprompters for automatic prompt optimization?▼

Teleprompters for automatic prompt optimization require defined metrics and training data to validate improvements; without measurable validation criteria and sufficient data, the optimizers cannot reliably evaluate or improve the modular AI system.

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