Build RAG systems and AI agents with declarative DSPy programming.
npx skills add https://github.com/automatedigital/spark --skill dspy-automatedigital
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/automatedigital/spark/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/automatedigital/spark --skill dspy-automatedigital
This Skill eliminates the tedious, error-prone work of manual prompt engineering and ad-hoc AI system building, enabling you to create reliable, maintainable AI pipelines that improve automatically over time.
Use the dspy skill to build a chain-of-thought question-answering module and optimize it with your existing FAQ dataset to improve answer accuracy for common user queries.
High-intent search queries and answers about installing and using this skill.
Automate prompt optimization by using data-driven teleprompters that systematically improve large language model performance without manual trial and error. You define AI tasks with type-safe signatures, and the framework automatically adjusts prompts to reduce error rates over time.
Declarative LM programming defines AI tasks using type-safe signatures instead of hand-writing prompts. It handles multi-stage pipelines by composing modular building blocks for RAG systems, AI agents, and classifiers, ensuring components remain portable and maintainable.
Yes, you can build a customer support RAG system that retrieves relevant documentation and generates accurate responses. By optimizing it with historical support ticket examples, the system improves automatically without adjusting prompts manually.
Automatic prompt optimization works with existing datasets by applying data-driven teleprompters. You optimize text classifiers and multi-stage pipelines using your historical data examples to systematically improve model accuracy and output validation.
Maintain consistent structured output validation by defining AI agents with type-safe signatures. This declarative approach replaces ad-hoc building with modular components, ensuring reliable and type-safe structured outputs across complex workflows.
Switch to declarative AI system building when manual prompt engineering becomes tedious and error-prone. If your complex AI workflows require reliable, maintainable pipelines that improve automatically over time, declarative LM programming provides the necessary modular composition.
DarkArty07Develop language model applications with declarative task definitions and automatic prompt optimization.
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Sameeh07Build RAG pipelines with LangChain for document ingestion and retrieval.
ovachieverDeclaratively program language models and automatically optimize prompts with DSPy.
