Build declarative language-model pipelines and optimize them with teleprompters.
npx skills add https://github.com/ricable/mcai --skill dspy-ricable
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/ricable/mcai/tree/main/.agents/skills/dspy Command: npx skills add https://github.com/ricable/mcai --skill dspy-ricable
DSPy removes the guesswork from prompt engineering by letting you build complex language-model systems declaratively and improve them with data-driven optimization instead of manual trial and error.
Use the dspy skill to design a small AI workflow for your task, then optimize it with a metric and a few representative examples.
High-intent search queries and answers about installing and using this skill.
Automatic prompt optimization uses data-driven teleprompters to tune instructions and demonstrations from training examples, replacing manual trial and error. You define a metric and a few examples, then run optimizers like BootstrapFewShot or MIPRO to improve pipeline reliability.
Declarative AI pipelines for RAG and agents are built using composable signatures and modules. This approach lets you define multi-stage workflows for retrieval, reasoning, and tool use, which can then be automatically optimized using evaluation metrics and training data.
Structured output extraction and classification workflows are defined using declarative signatures. By specifying the input and output schema directly, the pipeline enforces structured reasoning and validates outputs, which can then be optimized for accuracy with teleprompters.
Teleprompters can optimize multi-stage reasoning and tool-using agents by leveraging training examples and evaluation metrics. They automatically tune prompts and demonstrations within composable modules, improving the reliability of conditional control flow and agent actions.
Training examples and metrics are required for declarative programming to automatically optimize AI workflows. While you can define pipelines without them, data-driven optimization relies on these inputs to tune prompts and validate structured extraction or reasoning reliability.
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