Compose DSPy modules and prompts to build multi-stage AI pipelines.
npx skills add https://github.com/gigagiova/hermes-agent --skill dspy-gigagiova
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/gigagiova/hermes-agent/tree/main/skills/mlops/dspy Command: npx skills add https://github.com/gigagiova/hermes-agent --skill dspy-gigagiova
DSPy enables building complex AI systems by declaratively composing prompts, modules, and tools, reducing boilerplate and enabling modular re-use.
Create a simple DSPy module using a Predict signature and run it on a question-answer task to observe the output.
High-intent search queries and answers about installing and using this skill.
DSPy builds declarative LM pipelines by composing modules and prompts to reduce boilerplate. You use this approach when building multi-stage AI workflows like RAG pipelines, agents, or classifiers that require modular re-use in research and production.
Build a RAG pipeline by declaratively composing Predict, ChainOfThought, and ReAct modules. This modular composition orchestrates retrieval and generation stages, reducing boilerplate while enabling multi-stage workflows applicable in production environments.
Optimize prompts automatically using DSPy teleprompters optimizers like BootstrapFewShot, MIPRO, and COPRO. These optimizers improve prompts and pipeline performance through data-driven improvements rather than manual prompt tuning.
Yes, declarative modules like ReAct and ProgramOfThought build multi-stage agents and classifiers. These components orchestrate complex AI systems by composing prompts and tooling modularly for both research and production environments.
Structure modular AI systems by declaratively composing signatures and components like Predict and ChainOfThought. This approach eliminates prompt boilerplate by automating module orchestration and enabling modular re-use across complex workflows.
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RedWoodOGCompose prompts and modules to build AI systems with automatic optimization.
