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SriRamkunamsetty
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dspy

Automate AI pipeline construction with declarative LM programming and prompt optimization.

Updated May 20, 2026
Search Tags:#rag#agents#prompt-engineering#dspy#ai-pipelines#lm-programming
One-click install
npx skills add https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent --skill dspy-sriramkunamsetty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent/tree/main/hermes-agent/skills/mlops/research/dspy
Command: npx skills add https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent --skill dspy-sriramkunamsetty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy enables developers to build complex AI systems by programming language models declaratively and auto-optimizing prompts and workflows.

Core Features & Use Cases

  • Declarative LM programming for modular AI pipelines, including RAG, agents, and multi-stage workflows.
  • Automatic prompt optimization and lightweight evaluation to improve outputs with data.
  • Easy composition of modules and LM providers, enabling scalable, maintainable AI systems.

Quick Start

Define a DSPy module, configure an LM provider, and run a simple Predict workflow to generate an answer.

Frequently Asked Questions about dspy

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

FAQPage Schema
How do I build AI pipelines without manual prompt engineering?▼

You can build AI pipelines without manual prompt engineering by using declarative LM programming to define modular workflows and leveraging automatic prompt optimization to tune outputs with data.

What is declarative LM programming and how does it work for RAG systems?▼

Declarative LM programming automates the construction of end-to-end AI pipelines like RAG systems by defining modular components, allowing you to compose modules and LM providers to generate answers without manual prompt tuning.

Do I need an LM provider to use DSPy for multi-stage workflows?▼

Yes, you need an LM provider such as OpenAI or Anthropic to supply models and evaluation metrics when using DSPy to build multi-stage workflows and agents.

Can I compose multiple LM providers in a single agent workflow?▼

Yes, declarative LM programming enables easy composition of modules and LM providers, allowing you to build scalable and maintainable multi-stage agent workflows across software engineering contexts.

What is the best way to optimize prompts for complex AI agents?▼

The best way to optimize prompts for complex AI agents is using automatic prompt optimization and lightweight evaluation, which improves outputs with data rather than relying on manual prompt adjustments.

When should I not use manual prompt engineering for AI pipelines?▼

You should avoid manual prompt engineering when building complex, multi-stage AI pipelines that require scalable, maintainable module composition and automatic prompt optimization to consistently improve outputs.

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