Build declarative AI pipelines orchestrating LM tasks across providers.
npx skills add https://github.com/rnben/hermes-skills --skill dspy-rnben
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/rnben/hermes-skills/tree/main/plugins/mlops-skills/skills/dspy Command: npx skills add https://github.com/rnben/hermes-skills --skill dspy-rnben
DSPy enables developers to design and orchestrate end-to-end AI systems using a declarative programming model, replacing manual prompt engineering with structured pipelines.
Provide a task goal and DSPy will assemble a modular LM workflow.
High-intent search queries and answers about installing and using this skill.
You can build declarative AI pipelines by defining structured signatures for inputs and outputs, then assembling modular components like Predict, ChainOfThought, and ReAct to orchestrate complex multi-stage Q&A and agent workflows.
Replacing manual prompt engineering is best achieved using declarative programming models with structured signatures and optimizers, which automatically improve prompts and orchestrate tasks across diverse language model providers.
Yes, you can use modules like ChainOfThought and ReAct with local models, OpenAI, or Anthropic by applying provider configurations to orchestrate tasks across diverse deployment scenarios.
To set up retrieval-augmented generation, you integrate RAG support within your declarative pipeline and apply optimizers to systematically improve prompts and enhance the accuracy of document QA tasks.
ProgramOfThought works for building complex agent-based systems by allowing you to define structured task signatures and orchestrate modular workflows seamlessly across multiple language model providers.
You should use declarative LM pipelines instead of manual prompt construction when building scalable, modular AI systems like multi-stage Q&A or agent workflows, replacing fragile manual prompts with structured, optimizable signatures.
granietCompile task signatures into optimized language model pipelines with DSPy.
LamseyahEliasAutomate declarative LM programming and prompt optimization for modular AI pipelines.
BoraPerusicDesign and optimize RAG pipelines with embeddings, vector databases, and chunking strategies.
guccangOptimize language model programs with declarative task definitions and automated prompt improvement.
