dspy

Automate prompt optimization by using declarative LM programming to build data-driven pipelines. This approach programmatically refines prompts and constructs modular AI systems without manual tuning.

Declarative LM programming is a method to build complex AI systems by defining logic programmatically rather than manually. Use it when constructing modular pipelines that require prompt optimization or retrieval-augmented generation.

Build retrieval-augmented generation workflows by defining modular AI pipelines declaratively. This allows your language model to retrieve information from external data sources and generate coherent answers with minimal code.

Yes, you need the dspy, openai, and anthropic libraries installed. These dependencies provide the foundational framework for declarative programming, prompt optimization, and retrieval-augmented generation.

The best way to construct modular AI pipelines is through declarative LM programming. This method allows you to build complex question-answering systems that retrieve information and generate coherent answers programmatically.

Yes, you can use declarative LM programming to create RAG systems. It enables you to build pipelines that retrieve information from sources like the web and generate coherent answers automatically.