When should I use DSPy instead of standard prompting?

Use DSPy when your AI system involves multiple steps, requires high reliability, or when you want to move away from 'prompt hacking' toward a more systematic, engineering-focused approach to AI development.

What are DSPy Optimizers?

Optimizers (formerly Teleprompters) are algorithms that take your DSPy program and a small dataset to automatically generate effective prompts and few-shot examples that improve your model's performance on a specific task.

How does this skill help with RAG systems?

It provides structured modules for retrieval, reranking, and response generation, enabling you to build and optimize the entire RAG pipeline using specific metrics rather than manual prompt trial-and-error.

DSPy is a framework developed by Stanford NLP for programming language models by separating the program logic from the actual prompts, allowing for automatic, data-driven optimization of model behavior.

Can I use this with Anthropic Claude models?

Yes, the skill includes pre-configured patterns for integrating Claude Sonnet and other Anthropic models, as well as OpenAI and local models like Llama 3 via Ollama.

DSPy Declarative AI Programming

Name: DSPy Declarative AI Programming
Author: zechenzhangAGI

byzechenzhangAGI

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384

데이터 과학 및 ML

Builds complex AI systems using Stanford's declarative programming framework to optimize prompts and create modular RAG systems automatically.

소개

This skill integrates DSPy, the groundbreaking Stanford NLP framework, into your environment for systematic language model programming. It shifts focus from manual, fragile prompt engineering to defining clear program logic through Signatures and Modules, using data-driven optimizers to refine model behavior automatically. Whether you are building multi-stage AI pipelines, production-grade RAG systems, or self-correcting agents, this skill provides the implementation patterns and best practices needed to create reliable, portable, and high-performance AI applications across diverse model providers.

주요 기능

Advanced RAG system construction with retrieval, reranking, and self-consistency
Automatic prompt optimization using data-driven optimizers like MIPRO and BootstrapFewShot
384 GitHub stars
Modular patterns for Chain of Thought, ReAct, and Program of Thought reasoning
Declarative LLM programming via reusable Signatures and Modules
Multi-provider support including Anthropic Claude, OpenAI, and local Ollama models

사용 사례

Developing complex multi-stage agentic workflows that self-improve based on training data
Building production-grade RAG systems with automated retrieval and generation tuning
Systematically improving model accuracy through custom evaluation metrics and assertion-driven optimization

소개

주요 기능

Advanced RAG system construction with retrieval, reranking, and self-consistency
Automatic prompt optimization using data-driven optimizers like MIPRO and BootstrapFewShot
384 GitHub stars
Modular patterns for Chain of Thought, ReAct, and Program of Thought reasoning
Declarative LLM programming via reusable Signatures and Modules
Multi-provider support including Anthropic Claude, OpenAI, and local Ollama models

사용 사례

Developing complex multi-stage agentic workflows that self-improve based on training data
Building production-grade RAG systems with automated retrieval and generation tuning
Systematically improving model accuracy through custom evaluation metrics and assertion-driven optimization