dspy

Develops modular AI systems with TypeScript using the DDPIM framework and vocabulary-based reward models.

Updated Apr 16, 2026

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

Ready to run

npx skills add https://github.com/jacardl/New-Radar --skill dspy-jacardl

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy simplifies engineering of complex AI systems by enabling declarative LM programming, reducing the need for handcrafted prompts and ad-hoc wiring.

Core Features & Use Cases

Declarative signatures and modular building blocks (Predict, ChainOfThought, ReAct, ProgramOfThought)
Built-in optimizers (BootstrapFewShot, MIPRO, COPRO, BootstrapFinetune) for self-improving pipelines
RAG and agent orchestration with multi-stage workflows for reliable production ML systems

Quick Start

Install DSPy, define a simple DSPy signature, and run a basic example to see a structured output.

Frequently Asked Questions about dspy

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

FAQPage Schema

How do I build modular AI pipelines without handcrafting prompts?▼

You can build modular AI pipelines using declarative LM programming to define signatures and multi-step workflows, eliminating the need for ad-hoc prompt wiring. This approach ensures type-safe outputs and reliable production ML systems.

What is declarative LM programming for multi-step workflows?▼

Declarative LM programming models multi-step workflows by using modular building blocks like Predict, ChainOfThought, and ReAct. It simplifies engineering complex AI systems by shifting focus from manual prompt tuning to structured pipeline architecture.

How do I optimize RAG systems and tool-using agents for production?▼

You can optimize RAG systems and tool-using agents using built-in optimizers like BootstrapFewShot, MIPRO, and COPRO. These teleprompters enable self-improving pipelines for reliable production environments with extensible optimization.

Can I orchestrate multi-stage RAG workflows with type-safe outputs?▼

Yes, you can orchestrate multi-stage RAG workflows with type-safe outputs using modular building blocks. This declarative approach satisfies requirements for modularity and extensible optimization using DSPy’s teleprompters in production environments.

What's the best way to architect extensible AI agents in production environments?▼

The best way to architect extensible AI agents in production environments is using declarative signatures and modular components like ProgramOfThought. This enables reliable production ML systems with built-in optimizers for self-improving pipelines.

Why should I use declarative signatures instead of ad-hoc prompt wiring?▼

You should use declarative signatures instead of ad-hoc prompt wiring to reduce engineering complexity in AI systems. Built-in optimizers like BootstrapFinetune automatically improve pipeline performance, making multi-step workflows reliable and extensible.