DSPy Guide v2024.2.7-Python AI Framework Setup

Example

Imagine a scenario where a user needs to answer a question about historical events. The RAG module can first retrieve contextually relevant information using a Retrieve step, then generate an accurate, concise answer through a ChainOfThought step that synthesizes the final result.

Scenario

In research environments where analysts need to find precise answers quickly, RAG offers a way to combine search with language models to deliver accurate answers, making it ideal for fact-based queries.

Example

A teleprompter-based component, BootstrapFewShot helps developers compile DSPy pipelines by training models iteratively on a few high-quality examples. This reduces the manual effort of fine-tuning and ensures that DSPy solutions are highly accurate for their given task.

Scenario

Organizations that need to develop custom NLP models for domain-specific problems can use BootstrapFewShot to quickly refine their models using a small, curated set of examples, reducing time-to-market.

Example

LM Assertions enforce logical or factual constraints on the language model outputs. For instance, in a long-form question answering task, assertions can ensure that all answers include citations for each fact presented.

Scenario

When writing comprehensive reports, journalists and researchers can rely on LM Assertions to ensure that their outputs remain factual and align with their sources, maintaining high standards of integrity.