dspy

Build modular AI pipelines with DSPy signatures and optimizers.

150|25|Updated Apr 20, 2026

Search Tags:#self-improvement #rag #agents #declarative #prompt-optimization #dspy #lm-programming

Ready to run

npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill dspy-devsoul2026

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/Devsoul2026/Hermes-One-Click/tree/main/hermes-agent/skills/mlops/research/dspy
Command: npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill dspy-devsoul2026

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy provides a declarative programming model for building scalable, self-improving AI pipelines by describing tasks as precise signatures and modular components, reducing boilerplate and increasing reusability.

Core Features & Use Cases

Declarative LM programming with signatures and modular pipelines for robust AI systems.
Built-in optimizers (BootstrapFewShot, MIPRO, BootstrapFinetune, COPRO, KNNFewShot) for data-driven prompt and model improvements.
RAG, agent orchestration, and multi-step reasoning support for complex workflows in research and production.
Rich references and examples to guide implementation and experimentation.

Quick Start

Define a DSPy signature, instantiate a module, and run a prediction to observe DSPy in action.

Frequently Asked Questions about dspy

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

FAQPage Schema

How do I build self-improving AI pipelines without manual prompt engineering?▼

Declarative LM programming builds self-improving AI pipelines by defining tasks as precise signatures and modular components. This reduces boilerplate while allowing built-in optimizers to automatically refine prompts and model interactions.

What is the best way to optimize RAG components for production workflows?▼

The best way to optimize RAG components is using declarative LM programming with built-in optimizers like BootstrapFewShot and MIPRO. These data-driven tools automatically refine retrieval and generation pipelines across research and production contexts.

Can I orchestrate multi-step reasoning agents using declarative programming?▼

Yes, declarative LM programming supports agent orchestration and multi-step reasoning for complex workflows. You define modular pipelines with precise signatures, enabling scalable agent coordination across research and production environments.

Do I need a specific Python environment to use declarative LM programming?▼

You need a Python environment with the DSPy library installed and compatible LM providers configured. Optional optimization hooks and reference materials are available to guide implementation and experimentation within this setup.

What distinguishes declarative LM programming from traditional prompt engineering?▼

Declarative LM programming replaces manual prompt tuning with precise signatures and modular pipelines. Instead of rewriting prompts, you use built-in optimizers like BootstrapFewShot and COPRO to achieve data-driven, self-improving prompt and model enhancements.

When should I use automated prompt optimization over manual tuning?▼

Automated prompt optimization should be used for scalable, self-improving AI pipelines where reducing boilerplate and increasing reusability matter. It applies data-driven optimizers to RAG and agent workflows, replacing fragile manual prompt tuning.