dspy

Name: dspy
Availability: InStock
Author: attentiondotnet

Build complex AI pipelines declaratively with DSPy signatures and modular blocks.

Updated Mar 30, 2026

Search Tags:#rag #agents #modularity #declarative #prompts #lm-programming #optimizers

Ready to run

npx skills add https://github.com/attentiondotnet/hermes-agent --skill dspy-attentiondotnet

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy provides a structured approach to building, composing, and optimizing complex AI pipelines using declarative language model programming.

Core Features & Use Cases

Declarative LM programming with signatures and modular blocks
Data-driven prompt optimization and agents/RAG patterns
End-to-end system composition and deployment readiness
Multi-provider LM support with testing, evaluation, and orchestration

Quick Start

Install DSPy, define a minimal module with a signature, and run a simple QA workflow to validate the setup.

Frequently Asked Questions about dspy

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

FAQPage Schema

How do I build declarative LM pipelines for reliable AI systems?▼

Build declarative LM pipelines by defining structured signatures and modular blocks to compose multi-stage workflows. This approach provides telemetry to optimize prompts and orchestrate complex AI systems across multiple language model providers.

What is the best way to optimize prompts for multi-stage AI workflows?▼

Optimize prompts for multi-stage AI workflows using data-driven optimization features within declarative LM pipelines. This structured approach allows you to systematically refine prompts through telemetry and modular block composition rather than manual adjustments.

How do I orchestrate RAG and agents across multiple LM providers?▼

Orchestrate RAG and agents across multiple LM providers by composing modular blocks within a declarative pipeline framework. This enables end-to-end system design with deployment-ready configurations while maintaining multi-provider language model support.

Can I use modular blocks to compose end-to-end AI pipelines for deployment?▼

Yes, you can use modular blocks to compose end-to-end AI pipelines that are deployment-ready. By defining structured signatures and leveraging telemetry, you can orchestrate complex multi-stage workflows suitable for production environments across multiple LM providers.

Why does manual prompt engineering break down in complex AI pipelines?▼

Manual prompt engineering breaks down in complex AI pipelines because it lacks structured signatures and telemetry for systematic optimization. Declarative LM programming solves this by using modular blocks and data-driven optimizers to orchestrate reliable multi-stage workflows.