Automate declarative language-model programming to design and optimize AI pipelines.
npx skills add https://github.com/Hermesagents/hermes-agents --skill dspy-hermesagents
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/Hermesagents/hermes-agents/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/Hermesagents/hermes-agents --skill dspy-hermesagents
Building robust AI systems often requires stitching together disparate tools, prompts, and workflows in brittle code. DSPy provides a declarative framework to define task signatures and compose Predict, ChainOfThought, ReAct, and ProgramOfThought modules, enabling scalable, maintainable AI pipelines with self-improvement capabilities.
Make a simple DSPy module using Predict and run a basic QA example.
High-intent search queries and answers about installing and using this skill.
Automated prompt optimization is handled by teleprompters like BootstrapFewShot, MIPRO, and COPRO. These optimizers tune your language model pipelines data-driven, replacing manual prompt engineering with systematic weight and few-shot example adjustments.
Declarative machine learning programming involves defining task signatures and composing modules like Predict and ChainOfThought. This approach enables scalable, maintainable AI pipelines by abstracting brittle prompt stitching into structured, self-improving workflows.
Building RAG systems and agents is done by composing modular components for retrieval, reasoning, and tool-use. This framework orchestrates complex multi-stage workflows, replacing fragile custom code with maintainable, declarative AI pipelines.
ChainOfThought and ReAct modules are fully supported for complex reasoning tasks. You can compose these built-in modules alongside ProgramOfThought and TypedPredictor to orchestrate multi-stage workflows and ensure type-safe outputs.
Batch processing and exportable training data are supported for production readiness. You can configure language model providers, save and load modules, and export datasets to streamline deployment and evaluation of your AI pipelines.
Automated teleprompters should be used when building scalable, robust AI systems that require data-driven prompt optimization. If your workflow involves complex RAG, classifiers, or multi-stage agents, these optimizers replace brittle manual tuning with maintainable, self-improving pipelines.
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