Define typed signatures and compose pipelines with the DSPy framework.
npx skills add https://github.com/huidge/hermes-skills --skill dspy-huidge
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/huidge/hermes-skills/tree/main/mlops/research/dspy Command: npx skills add https://github.com/huidge/hermes-skills --skill dspy-huidge
Declarative LM programming solves the complexity of building complex AI systems by defining tasks as composable signatures instead of hard-coded prompts.
Try a minimal DSPy example by defining a simple signature and a Predict module to answer a question.
High-intent search queries and answers about installing and using this skill.
Declarative LM programming builds AI pipelines by defining tasks as composable signatures with typed inputs and outputs. This modular approach replaces ad-hoc prompts with reliable, reusable components for complex workflows.
Automatic prompt optimization uses teleprompters like BootstrapFewShot and MIPRO to improve prompts from data. This technique systematically refines language model instructions within declarative pipelines.
Yes, declarative LM programming constructs RAG systems, ReAct-style agents, and multi-stage workflows from modular components. These systems integrate retrieval, reasoning, and generation with safety guards.
Declarative LM programming requires the DSPy framework and works with providers like OpenAI and Anthropic. It enforces type-safe interfaces across these language model providers.
Type-safe signatures ensure modular composition and reliable interfaces for multi-stage language model pipelines. This declarative approach enables rapid construction of robust AI systems for research and production.
Teleprompter-based optimization is needed when building complex AI systems that require automated prompt refinement from data. It improves reliability in declarative pipelines handling routing and evaluation loops.
lxt070409Automate creation, optimization, and deployment of AI systems with DSPy.
john-data-chenDeclares tasks as signatures and composes modular AI pipelines with teleprompter optimizers.
jason660519Compose declarative language-model pipelines with signatures and optimization loops.
SignmanalDefine type-safe LM task signatures and automatically optimize prompts with teleprompters.
