Build modular AI pipelines with declarative DSPy programming.
npx skills add https://github.com/samuelmukoti/myai-agent --skill dspy-samuelmukoti
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/samuelmukoti/myai-agent/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/samuelmukoti/myai-agent --skill dspy-samuelmukoti
DSPy provides a declarative programming model for building complex, modular AI pipelines that orchestrate LM-powered components (signatures, modules, and agents) with built-in optimization and tooling, reducing boilerplate and improving reliability.
Install DSPy, define a simple module, and run a basic prediction to see DSPy in action.
High-intent search queries and answers about installing and using this skill.
Declarative LM programming uses signatures and modules to compose multi-stage AI pipelines, reducing boilerplate and improving reliability. It orchestrates LM-powered components for systems like RAG, agents, and classifiers with built-in optimization.
You optimize prompts for RAG systems and agents using built-in teleprompters like BootstrapFewShot, MIPRO, and COPRO. These optimizers automatically improve prompt behavior and model performance within modular DSPy pipelines.
Yes, you can use multiple LM providers in a single multi-stage AI workflow. The framework supports cross-LM orchestration across providers, allowing different stages of your pipeline to leverage different models.
Yes, this approach works for generating type-safe, structured outputs. TypedPredictor provides structured outputs for robust integrations and IDE support, ensuring typed modules are exported for reuse across different components.
Yes, you need to install the DSPy core library to build modular AI pipelines. Optional teleprompters are also available for optimization if you want to automatically improve prompt behavior and model performance.
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