dspy

To stop manually tuning prompts, use declarative programming to define inputs and outputs as structured signatures, then apply data-driven optimizers like BootstrapFewShot or MIPRO to automatically compile and refine few-shot examples for your language model pipelines.

The best way to build a multi-step RAG pipeline is using modular components to structure retrieval, reasoning, and evaluation stages. This declarative approach allows you to assemble classifiers and agent workflows into reliable, reusable systems without brittle hand-tuned prompts.

Yes, you need to define evaluation metrics to optimize language model programs effectively. Automatic optimization relies on these metrics to assess performance, allowing data-driven optimizers to iteratively improve prompts and few-shot examples within your AI pipelines.

Declarative programming replaces trial-and-error prompt engineering by defining inputs and outputs via structured signatures rather than hand-tuning prompts. This approach makes AI workflows easier to maintain, optimize, and reuse by compiling modular components into reliable systems.

Yes, you can turn a brittle prototype into an optimized retrieval augmented generation system by applying modular components, automated optimizers, and evaluation metrics. This transforms unreliable question-answering prototypes into robust, reusable retrieval pipelines with reranking.