Build end-to-end LM pipelines with declarative DSPy components and optimizers.
npx skills add https://github.com/peteromallet/megaplan --skill dspy-peteromallet
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/peteromallet/megaplan/tree/main/megaplan/agent/skills/mlops/research/dspy Command: npx skills add https://github.com/peteromallet/megaplan --skill dspy-peteromallet
DSPy provides a declarative, modular framework to build, test, and optimize complex LM systems, reducing manual prompt engineering and boosting reliability.
Create a basic QA pipeline and optimize it with BootstrapFewShot on a small trainset, then evaluate on a held-out set.
High-intent search queries and answers about installing and using this skill.
Build declarative LM pipelines using modular components like Predict, ChainOfThought, and ReAct to structure multi-step reasoning and orchestrate complex agent workflows with type safety and traceability.
Automate prompt optimization by applying built-in optimizers like BootstrapFewShot, MIPRO, and COPRO to train datasets, which reduces manual prompt engineering and improves model performance reliability.
Declarative programming supports end-to-end RAG, classification, and multi-stage pipelines by using modular signatures and guardrails to simplify complex AI workloads and ensure structured outputs.
Yes, you need Python and the DSPy library to utilize modules like ProgramOfThought and automated optimizers for building and testing robust, modular LM systems.
Declarative AI programming reduces manual prompt engineering by replacing hand-crafted prompts with modular signatures and automated optimizers that iteratively compile and refine LM behavior.
Use automated optimizers like BootstrapFinetune when you need to improve data efficiency and model performance on held-out evaluation sets, moving beyond basic prototyping to robust production systems.
ihatesea69Develop AI systems with declarative programming and automatic prompt optimization in DSPy.
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icyzhCompile declarative language model calls into self-improving pipelines with automated optimization.
