Optimize DSPy prompts and pipelines using teleprompters and training data.
npx skills add https://github.com/jleechanorg/hermes-agent --skill dspy-jleechanorg
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/jleechanorg/hermes-agent/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/jleechanorg/hermes-agent --skill dspy-jleechanorg
DSPy optimizes prompts and orchestrates declarative LM programming to turn prompt engineering into data-driven, self-improving pipelines. It supports multi-stage systems, tool use, and optimization across multiple LMs for complex reasoning tasks in research, development, and production. This skill requires a DSPy module, a defined metric, and training data to perform bootstrap few-shot, MIPRO, or finetuning teleprompter optimization and evaluation.
Create a minimal DSPy workflow with a simple ChainOfThought module and run a basic prompt to observe the reasoning steps and final answer.
High-intent search queries and answers about installing and using this skill.
Automating prompt optimization for multi-stage pipelines requires defining a DSPy module, a metric, and training data to run teleprompter optimizations like BootstrapFewShot or MIPRO, turning manual prompt engineering into data-driven workflows.
Declarative LM programming defines tasks using signatures and modular blocks that compose into complex workflows, needed when building multi-stage systems, agents, or parallel pipelines for high-assurance reasoning tasks in research and production.
You can build RAG systems with tool use by defining modular blocks and signatures, orchestrating multi-stage systems with traceability, and relying on the framework's built-in error handling for production-grade workflows.
Training data and a defined metric are required to perform prompt optimization, as teleprompters like BootstrapFewShot, MIPRO, and finetuning rely on these inputs to evaluate and generate better prompts and demonstrations.
The best way to start is by creating a minimal DSPy workflow using a simple ChainOfThought module, running a basic prompt to observe the reasoning steps and final answer before applying advanced optimization.
Unlike manual prompt engineering, this approach uses teleprompters to automatically generate and select better prompts and demonstrations via bootstrap few-shot, MIPRO, or finetuning optimization, enabling self-improving pipelines for complex reasoning tasks.
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