Automate building declarative LM pipelines with DSPy.
npx skills add https://github.com/dawsonblock/HERMY --skill dspy-dawsonblock
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/dawsonblock/HERMY/tree/main/hermes-agent-2026.4.23/skills/mlops/research/dspy Command: npx skills add https://github.com/dawsonblock/HERMY --skill dspy-dawsonblock
DSPy enables building complex AI systems by declaratively composing language-model tasks into modular pipelines, reducing manual prompt tuning and enabling self-improving workflows.
Define a simple DSPy module with a QA signature and run BootstrapFewShot to optimize prompts on a tiny trainset.
High-intent search queries and answers about installing and using this skill.
Build declarative LM pipelines by composing modular tasks with type-safe signatures. This approach reduces manual prompt engineering and enables self-improving workflows for complex RAG and agent systems.
Build a multi-stage RAG pipeline by defining declarative, type-safe input and output signatures for each LM task. These modular components are then composed together, ensuring JSON-serializable configurations for production-grade AI systems.
Yes, you can use DSPy optimizers like BootstrapFewShot, MIPRO, and COPRO with OpenAI and Anthropic models. The framework supports declarative LM programming across these providers to automate prompt and data optimization.
The best way to create self-improving AI agents without manual prompt tuning is using declarative LM programming with built-in optimizers. This automates prompt and data optimization, allowing modular agent pipelines to improve themselves over time.
Yes, declarative LM programming works for both research and production tasks. It supports modular composition, multi-stage pipelines, and JSON-serializable configurations, satisfying requirements for scalable AI systems from experimentation to deployment.
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