dspy

Declarative LM programming streamlines AI workflow automation by compiling modular language model calls into self-improving pipelines, enabling systematic task structuring and automatic reasoning steps without manual prompt tuning.

You can optimize prompts for RAG systems by applying data-driven optimization methods to refine reasoning chains, allowing the framework to automatically enhance model responses and retrieval performance.

Yes, you can build multi-component AI agents by using OpenAI and Anthropic dependencies to define task structures, execute reasoning chains, and process data within modular pipelines.

The best way to structure complex AI pipelines is through modular design, where you define declarative task structures and apply optimizers to enhance data processing and model responses.

Yes, compiling self-improving pipelines requires the dspy, openai, and anthropic dependencies to support automatic reasoning steps, prompt optimization, and language model interactions.

You should avoid declarative programming when your language model tasks require highly customized, non-standard reasoning flows that cannot be easily modularized or optimized through data-driven pipeline compilation.