dspy
Category: llm-applications · License: MIT · Version: 1.0.0 · Includes scripts
Tags: Prompt Engineering, DSPy, Declarative Programming, RAG, Agents, Prompt Optimization, LM Programming, Stanford NLP, Automatic Optimization, Modular AI
When to use it
Builds and optimizes language model programs with DSPy (Stanford NLP), using Signatures, modules (Predict, ChainOfThought, ReAct, ProgramOfThought) and optimizers (BootstrapFewShot, MIPRO, BootstrapFinetune). Use when replacing hand-written prompts with declarative signatures, when automatically tuning prompts against training data and a metric, when building multi-stage RAG pipelines or ReAct agents, when building classifiers or structured-output programs, or when saving and evaluating optimized LM programs across providers such as Anthropic, OpenAI, or Ollama. Not for quick prototypes or simple chains; use manual prompting or LangChain instead.Example requests
Ask your agent in plain language, for example:- “How do I turn a dspy Signature into an optimized program?”
- “I have a hand-written few-shot prompt in prompts/triage.txt that classifies 2,000 clinical notes in notes_labeled.csv into 6 urgency classes with claude-sonnet. Accuracy is stuck around 71%. I’d like to port it to DSPy and use MIPROv2 to tune it, and I keep getting ‘AttributeError: module dspy has no attribute OpenAI’ when I configure the LM. Can you help?”
- “I’m building a question-answering system over our lab’s 5,000 protocol PDFs. The retrieval step then answer generation needs to be tuned automatically against about 150 question/answer pairs I wrote, so I don’t have to keep rewriting prompts by hand every time we swap the underlying model. What’s a good approach?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole llm-applications category with /plugin install llm-applications@research-agent-skills.