DSPy Guide v2024.1.31-Customizable NLP Framework Guide

Example

Developers can implement the ChainOfThought module to break down complex questions into simpler reasoning steps. This is particularly useful in enhancing the clarity and traceability of answers provided by LMs.

Scenario

In a question-answering system, using ChainOfThought to deduce the capital of a country by first identifying the country's continent, then narrowing down to countries within that continent, and finally selecting the capital.

Example

The teleprompt feature allows for the design and optimization of prompts to improve interaction with LMs. This includes the BootstrapFewShot technique for refining the accuracy and relevance of responses based on a set of training examples.

Scenario

Optimizing a customer service chatbot's responses by training it with a set of common customer queries and preferred answers, ensuring the chatbot provides the most accurate and helpful responses.

Example

This feature enables developers to assert computational constraints and provide soft suggestions to guide the LM towards more accurate or contextually appropriate outputs.

Scenario

In a content generation tool, using LM Assertions to ensure generated texts adhere to factual accuracy and using soft suggestions to maintain a consistent tone and style throughout the text.