Define type-safe LM task signatures and automatically optimize prompts with teleprompters.
npx skills add https://github.com/Signmanal/VIGIL --skill dspy-signmanal
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/Signmanal/VIGIL/tree/main/optional-skills/mlops/research/dspy Command: npx skills add https://github.com/Signmanal/VIGIL --skill dspy-signmanal
This Skill eliminates the tedious, error-prone work of manual prompt engineering and fragile, hard-to-maintain language model pipelines, enabling developers to build reliable AI systems faster.
Use the dspy skill to create an optimized question-answering pipeline that uses chain-of-thought reasoning and automatically improves its prompts with your provided training examples.
High-intent search queries and answers about installing and using this skill.
To stop manual prompt engineering, use a declarative programming framework that defines type-safe task signatures for inputs and outputs, letting the system automatically tune prompts instead of requiring hand-crafted instructions.
Automatic prompt optimization uses data-driven teleprompters to tune retrieval and generation prompts. It works with RAG systems by systematically improving prompt performance using your provided training examples and document datasets.
Build multi-stage reasoning workflows by composing modular, reusable components within a declarative LM programming framework, ensuring your AI pipelines remain portable and reproducible across different LLM providers.
Yes, you can optimize AI agent development by using data-driven teleprompters that automatically tune prompts and few-shot examples, cutting manual tuning time from hours to minutes using your specific training data.
No, declarative AI programming eliminates manual few-shot example selection by using automatic prompt optimization to generate and tune few-shot examples data-dependently, replacing fragile manual language model pipeline maintenance.
You should avoid manual prompt engineering for text classifiers when tasks require systematic prompt optimization, multi-stage reasoning, or modular component composition, as manual tuning becomes fragile and hard to maintain at scale.
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bobmatnycAutomate prompt optimization with a declarative DSL for LLM workflows.
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