dspy

Declarative language model programming defines AI tasks using reusable signatures and modules instead of hardcoded prompts, allowing you to build reliable LM pipelines that can be systematically optimized and maintained.

You can optimize prompts automatically by applying DSPy optimizers driven by evaluation metrics and training examples, which systematically improve model behavior and few-shot examples without manual tuning.

Yes, you can build optimized RAG systems by defining retrieval and generation stages as declarative modules, then using evaluation data to systematically improve the reliability of the entire pipeline.

The best way to structure multi-stage AI reasoning pipelines is by defining each stage as a declarative module with specific signatures, enabling systematic evaluation and optimization of the overall language model workflow.

Yes, training examples and evaluation metrics are required to drive the automatic optimization workflows that improve language model application reliability and systematically enhance prompt behavior.