dspy

To build reliable LLM workflows without manual prompt engineering, use declarative signatures to define inputs and outputs, composable modules for structure, and optimization metrics to automatically generate and improve few-shot examples and instructions.

The best way to optimize prompts for retrieval-augmented generation and multi-step reasoning is applying automatic prompt optimization, which uses training data and validation metrics to iteratively evaluate and improve LLM program instructions and few-shot examples.

Yes, you can use OpenAI and Anthropic models to build multi-step reasoning agents by defining declarative signatures and composable modules that structure tool usage and retrieval-augmented generation within your LLM programming pipelines.

To design a DSPy pipeline for question answering and structured extraction, define a declarative signature, choose composable modules, integrate retrieval or tool usage if needed, and establish an optimization metric to evaluate performance on a validation set.

You should replace manual prompt crafting with structured language model programs because declarative signatures and composable modules make LLM applications easier to maintain, test, and iteratively improve using automatic prompt optimization.

Yes, this approach works for classification and multi-stage reasoning pipelines by using declarative signatures and composable modules to structure the workflows, while applying optimization metrics to automatically evaluate and improve performance.