Reviewing the Dataset

In this chapter, you'll learn how to carefully examine a collection of around 200 question-answer pairs derived from your project's documentation. This hands-on approach will help you grasp the nuances of how your AI model interprets and extracts information from the original text.

You’ll dive into specific examples where answers are either too brief or slightly inaccurate, highlighting areas for improvement. For instance, an answer might be technically correct but lack essential context that would make it more useful to users. By identifying these gaps, you can refine your dataset to ensure it provides comprehensive and accurate information.

Additionally, the chapter covers how to handle irrelevant question-answer pairs that don’t pertain to the core content of your project. This insight will help you fine-tune your model’s ability to distinguish between relevant and extraneous data.

This review process is crucial for enhancing the quality and reliability of your AI application's responses. Dive in to see how a thorough dataset review can elevate your project!