Compile declarative language model calls into self-improving AI pipelines.
npx skills add https://github.com/zeronx798/demo-hermes-agent --skill dspy-zeronx798
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/zeronx798/demo-hermes-agent/tree/main/optional-skills/mlops/research/dspy Command: npx skills add https://github.com/zeronx798/demo-hermes-agent --skill dspy-zeronx798
This skill addresses the fragility and manual labor of traditional prompt engineering by providing a framework to program language models declaratively, allowing for systematic, data-driven optimization of AI pipelines.
Use the dspy skill to configure a Claude model and define a signature for a question-answering module.
High-intent search queries and answers about installing and using this skill.
Automate prompt optimization by using data-driven teleprompters to systematically refine prompts and few-shot examples, compiling declarative language model calls into self-improving pipelines for complex AI tasks.
Declarative language model programming replaces the fragility and manual labor of traditional prompt engineering by allowing systematic, data-driven optimization of AI pipelines using modular components like Predict, ChainOfThought, and ReAct.
Build a modular RAG system by defining declarative signatures and reusable components, then use automatic prompt optimization to refine retrieval and generation stages based on a validation dataset for higher accuracy.
Yes, executing optimized reasoning chains requires integration with language model providers like OpenAI or Anthropic to run the compiled declarative language model pipelines.
Yes, construct maintainable AI agents by assembling modular, reusable components like Predict, ChainOfThought, and ReAct into complex pipelines, eliminating the need for hardcoded prompts.
Automatic prompt optimization requires a validation dataset to function and depends entirely on external language model providers like OpenAI or Anthropic to execute the compiled reasoning chains.
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