Compile typed signatures and modules against defined metrics to optimize language model prompt programs.
npx skills add https://github.com/magnus919/agent-skills --skill dspy-magnus919
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/magnus919/agent-skills/tree/main/dspy Command: npx skills add https://github.com/magnus919/agent-skills --skill dspy-magnus919
This skill solves the instability and manual labor of traditional prompt engineering by treating prompt programs as compilable code that can be automatically optimized against specific metrics.
Use the dspy skill to compile a ChainOfThought program against the provided training dataset to optimize its performance.
High-intent search queries and answers about installing and using this skill.
You can optimize LLM prompt programs by compiling typed signatures and modules against defined metrics using the dspy framework. This treats prompts as compilable code, automatically tuning instructions and few-shot examples via Bayesian or evolutionary search.
The best way to fix a failing multi-step reasoning pipeline is to compile your program against a validation dataset. This systematically finds the optimal prompt instructions and few-shot examples to maximize accuracy, eliminating manual string tuning.
Yes, dspy works with Python to build complex agentic tool-use workflows. It uses a modular architecture with typed signatures and Python control flow, replacing brittle string concatenation with structured, compilable program logic.
To compile a ChainOfThought program, compile it against a provided training dataset using the dspy framework. This manages program structure, optimizer selection, and evaluation loops to automatically discover the best prompt instructions.
You should use programmatic prompt optimization when you face instability and manual labor in traditional prompt engineering. It is ideal for multi-step reasoning tasks, allowing you to systematically compile and evaluate typed modules against validation data.
Yes, you can use Bayesian or evolutionary search to tune few-shot examples and instructions for language model pipelines. The dspy framework manages these optimization loops to maximize your program's accuracy against defined metrics.
QredenceIntegrate DSPy with the Microsoft Agent Framework for typed signatures and orchestration.
afel6Build declarative language-model programs and optimize prompts for RAG pipelines.
maruakshayIdentify and mitigate security risks in DSPy prompt programs and tool wrappers.
aisa-groupOptimize and structure prompts for consistent LLM outputs.
