dspy

Name: dspy
Availability: InStock
Author: mesferali

Program large language models declaratively with signatures and automatic prompt optimization.

Updated Jan 12, 2026

Search Tags:#prompt engineering #rag #agents #dspy #declarative ai #lm programming

Ready to run

npx skills add https://github.com/MesferAli/XCircle --skill dspy-mesferali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/dspy
Command: npx skills add https://github.com/MesferAli/XCircle --skill dspy-mesferali

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dspy, openai, anthropic, and includes references (resource) components.

What problem does it solve?

This Skill tackles the challenges of complex AI system development, manual prompt engineering, and inconsistent LM outputs by providing a declarative and optimizable framework.

Core Features & Use Cases

Declarative Programming: Define AI tasks using signatures, not just raw prompts.
Automatic Prompt Optimization: Improve LM performance systematically using data-driven methods.
Modular RAG & Agents: Build robust, reusable AI components.
Use Case: Develop a customer support chatbot that can understand user intent, retrieve relevant documentation, and generate helpful responses, all while being systematically optimized for accuracy and helpfulness.

Quick Start

Use the dspy skill to build a question-answering system by defining a signature and using a Predict module.

Frequently Asked Questions about dspy

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

FAQPage Schema

How do I optimize prompts automatically instead of manual prompt engineering?▼

Automatic prompt optimization is achieved by using data-driven methods and optimizers to systematically improve large language model performance. You replace manual tuning with declarative signatures and performance enhancers to refine outputs.

How do I build a modular RAG system using declarative AI programming?▼

Building a modular RAG system involves defining AI tasks with declarative signatures and using modules like Predict or ChainOfThought. This approach creates robust, reusable components for retrieving documentation and generating responses systematically.

Does DSPy work with OpenAI and Anthropic models for agent development?▼

Yes, DSPy supports agent development with both OpenAI and Anthropic models. You can program complex AI systems using modules like ReAct and ProgramOfThought across these supported language model providers.

What is declarative LM programming and when do I need it for complex AI systems?▼

Declarative LM programming defines AI tasks using signatures rather than raw prompts. You need it when building complex AI systems to overcome manual prompt engineering challenges and ensure consistent, optimizable language model outputs.

Why does my large language model output fluctuate and how can I make it consistent?▼

Large language model output fluctuates due to manual prompt engineering and inconsistent tuning. You can make it consistent by adopting a declarative programming framework with automatic prompt optimization to systematically control performance.

Are there limitations to using signatures instead of raw prompts for AI tasks?▼

Using signatures instead of raw prompts shifts control to a declarative framework. Limitations arise when tasks lack clear input-output definitions, making it harder to apply automatic prompt optimization and modular components like Predict or ReAct effectively.