dspy

Builds AI systems with declarative language-model programming and typed outputs.

Updated Jul 7, 2026
One-click install
npx skills add https://github.com/TitoPrausee/nexus-toti --skill dspy-titoprausee
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/TitoPrausee/nexus-toti/tree/main/data/skills/mlops/research/dspy
Command: npx skills add https://github.com/TitoPrausee/nexus-toti --skill dspy-titoprausee

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy removes the need to hand-tune brittle prompts when building multi-step language model applications, making it easier to create reliable AI workflows that improve from data.

Core Features & Use Cases

  • Declarative task signatures for structured inputs and outputs.
  • Reusable modules for prediction, chain-of-thought reasoning, tool use, and typed extraction.
  • Optimizers like BootstrapFewShot, MIPRO, and BootstrapFinetune for systematic prompt improvement.
  • Well suited for RAG systems, research agents, classifiers, summarizers, and multi-stage support workflows.
  • Example use case: convert a manual research assistant into an optimized agent that retrieves sources, reasons over evidence, and returns validated answers.

Quick Start

Use the dspy skill to turn a question-answering workflow into an optimized multi-stage pipeline with retrieval, reasoning, and tool use.

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 without manual tuning?

Optimize language model prompts systematically by using declarative signatures and optimizers like BootstrapFewShot or MIPRO. This removes the need to hand-tune brittle prompts by compiling declarative modules into improved instructions using evaluation metrics.

What is the best way to build reliable AI agents for research workflows?

Build reliable AI agents for research workflows by using declarative language-model programming to create reusable modules for retrieval, chain-of-thought reasoning, and tool use, ensuring systems improve systematically from data.

How do I create a RAG pipeline with multi-step reasoning?

Create a RAG pipeline with multi-step reasoning by defining declarative task signatures for structured inputs and outputs, then chaining reusable prediction and reasoning modules to retrieve sources and validate answers.

Can I use declarative programming for structured extraction and classification?

Declarative programming supports structured extraction and classification by defining typed outputs and signatures. Reusable modules handle prediction and extraction, allowing systematic prompt optimization for reliable deployment.

Does systematic prompt optimization work for multi-stage support workflows?

Systematic prompt optimization works for multi-stage support workflows by applying optimizers like BootstrapFinetune to reusable modules, enabling pipelines to improve from data and yield validated, typed outputs.