Improving the Retrieval

In this chapter, you'll dive into refining your AI application's performance. You’ll start by addressing the messy data issue in your knowledgebase, where chunks from different documents are mixed up and not always relevant to user queries.

Firstly, we tackle incorrect embeddings by ensuring that each chunk is properly labeled as either a document or a query. This simple fix significantly improves how the language model interprets and responds to questions.

Next, you'll learn to merge these scattered chunks into a more coherent structure before feeding them to your AI. By organizing data based on file names and headlines, you’ll provide clearer context for the model, leading to better answers.

Throughout this chapter, we also explore ways to validate improvements through various user queries, highlighting how adjustments in embeddings and chunk organization can dramatically enhance response accuracy. However, it's important to recognize that even with these enhancements, your dataset might still be lacking if it’s not structured specifically for question-answering tasks.

Join us as we delve into the intricacies of improving retrieval systems and discover practical steps to elevate your AI application's performance!