prompt-engineering-dspy

Topics in the source

• DSPy: Declarative Language Model Programming
• When to Use This Skill
• Installation
• Quick Start
• Basic Example: Question Answering
• Chain of Thought Reasoning
• Core Concepts
• 1. Signatures

Use DSPy when you need to: Build complex AI systems with multiple components and workflows Program LMs declaratively instead of manual prompt engineering Optimize prompts automatically using data-driven methods Create modular AI pipelines that are maintainable and portable Improve model outputs systematically with optimizers Build RAG systems, agents, or classifiers with better reliability GitHub Stars: 22,000+ | Created By: Stanford NLP Signatures define the structure of your AI task (inputs → outputs): When to use each: Inline: Quick prototyping, simple tasks Class: Complex tasks, type hints, better documentation Modules are reusable components that transform inputs to outputs: Basic prediction module: Generates reasoning steps before answering: Agent-like reasoning with tools: Generates and executes code for reasoning: Optimizers improve your modules automatically using training data: Learns from examples: Iteratively improves prompts: Creates datasets for model fine-tuning: | Feature | Manual Prompting | LangChain | DSPy | |---------|-----------------|-----------|------| | Prompt Engineering | Manual | Manual | Automatic | | Optimization | Trial & error | None | Data-driven | |

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