dspy_skill

Overview

This skill packages DSPy, a declarative framework from Stanford NLP for building and optimizing complex language-model systems. It helps you define structured tasks, compose modular pipelines (RAG, agents, multi-stage flows), and automatically optimize prompts and modules using data-driven optimizers. Use it to move from brittle prompt engineering to reproducible, testable LM programs.

How this skill works

DSPy exposes Signatures, Modules, and Optimizers so you can declare inputs→outputs, compose components, and run modules like Predict, ChainOfThought, ReAct, or custom RAG pipelines. Optimizers such as BootstrapFewShot, MIPRO, and BootstrapFinetune evaluate modules on training examples and search for better prompts, few-shot demos, or finetune datasets. You configure LM providers (Anthropic, OpenAI, local models) and evaluate with custom metrics to iterate toward reliable behavior.

When to use it

• When you need to build multi-component AI systems (RAG, agents, pipelines).
• When you want declarative, typed task definitions instead of ad-hoc prompts.
• When you have training/validation data and want automatic prompt optimization.
• When you need reliable reasoning pipelines (chain-of-thought, program-of-thought).
• When you want modular, portable components with clear interfaces.

Best practices

• Start simple: prototype with dspy.Predict, then add ChainOfThought or ReAct as needed.
• Use descriptive Signatures and typed OutputFields to enforce structure and make debugging easier.
• Optimize only with representative training and validation sets; use clear metrics (exact match, F1).
• Use cheaper LMs for retrieval and stronger LMs for reasoning via settings.context to save cost.
• Enable tracing during development to inspect prompts, responses, and backtracking behavior.

Example use cases

• Build a RAG-based question answering system that retrieves, reranks, and answers with ChainOfThought reasoning.
• Create an agent that uses ReAct with search or custom tools for grounded information access.
• Optimize a short-answer QA predictor with BootstrapFewShot to improve exact-match accuracy.
• Generate programmatic solutions with ProgramOfThought for calculational or code-generating tasks.
• Produce structured information extraction using TypedPredictor and Pydantic schemas.