Build modular AI pipelines with declarative LM programming and automated prompt optimization.
npx skills add https://github.com/ever-oli/io --skill dspy-ever-oli
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/ever-oli/io/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/ever-oli/io --skill dspy-ever-oli
DSPy addresses the complexity of building robust, multi-component AI systems by offering declarative language model programming and automated prompt optimization, enabling safer composition of tools, signatures, and modules.
Create a DSPy module with a signature and run the built-in teleprompters to optimize prompts and compose RAG and agent pipelines.
High-intent search queries and answers about installing and using this skill.
Declarative LM programming uses type-safe signatures and modular pipelines to build multi-stage workflows, enabling safer composition of tools and modules for RAG systems and agent-based automation without manual prompt engineering.
You optimize prompts automatically by defining a DSPy module with a signature and running built-in teleprompters like BootstrapFewShot, MIPRO, or COPRO, which use data-driven optimization to improve prompts and model behavior end-to-end.
Yes, you can build multi-stage RAG systems and agent-based automation by composing modular pipelines and declarative signatures, ensuring type-safe interactions and data-driven optimization across diverse language model providers.
Yes, declarative LM programming supports multi-provider LM integration, allowing you to develop and deploy end-to-end production workflows that evaluate and deploy pipelines across diverse language model providers.
Declarative signatures replace manual prompt engineering by providing type-safe interfaces and automated prompt optimization, allowing you to build self-improving AI pipelines with safer composition of tools and modules.
Automated prompt optimization requires sufficient training data for teleprompters like BootstrapFewShot and MIPRO to function effectively, and complex multi-stage workflows may need careful evaluation to ensure production-grade performance.
thinking-machines-labConfigure and run Direct Preference Optimization training with a model and preference dataset.
melikhanmutluMeasures LLM-based application performance via automated metrics, human judgments, and benchmarking.
devCharuzuDesign and deploy production-grade RAG pipelines with chunking, embeddings, vector stores, and evaluation.
saeed-vayghanDesign, evaluate, and manage prompt architectures for large language models.
