Build declarative AI systems with DSPy signatures and automatic optimizers.
npx skills add https://github.com/musical-basics/hermes-build-2 --skill dspy-musical-basics
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/musical-basics/hermes-build-2/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/musical-basics/hermes-build-2 --skill dspy-musical-basics
Building complex AI systems often requires hand‑crafted prompts, brittle pipelines, and repetitive experimentation. This Skill provides a declarative framework that compiles language‑model calls into maintainable, optimizable modules.
Use the dspy skill to generate a question‑answering module with automatic few‑shot prompting.
High-intent search queries and answers about installing and using this skill.
Automatic prompt optimization uses optimizers like BootstrapFewShot and MIPRO to compile language-model calls into optimized modules by generating few-shot examples, eliminating the need for manual prompt tuning and brittle experimentation.
Declarative AI pipelines use Python signatures and modules to define inputs and outputs for components like ChainOfThought and ReAct, compiling language-model calls into maintainable, optimizable systems rather than hand-crafting prompts.
You can build RAG pipelines and multi-stage agents by assembling reusable modules such as Predict, ChainOfThought, and ReAct into end-to-end workflows with verification loops for robust production systems.
Declarative LM programming works across OpenAI, Anthropic, and local LLMs, allowing you to apply signatures, few-shot examples, and automatic optimizers to create prompt-optimized models within a unified framework.
Hand-crafted prompts create brittle pipelines that fail when models change, but declarative modules with automatic optimizers compile and regenerate few-shot examples to maintain performance across different language models.
DSPy requires Python to define inputs and outputs using Python classes and signatures, enabling reusable components and automatic optimizers to generate few-shot examples for improved performance.
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