Introduction

You’ve seen SportsBuddy — your sports enthusiast RAG chatbot — in action. It performs admirably as a basic RAG app. But how can you make it even better? Imagine you’re ready to take it to the market and launch it as SportsBuddy Pro. :]

For instance, you can enhance retrieval efficiency by batching data retrieval from multiple sources, fine-tuning prompts (especially with the chat history), adding citations and sources to responses, and leveraging faster, more cost-effective, and accurate LLMs and their components. You could even implement response scoring and many other improvements. There’s a wealth of potential waiting to be unlocked in the current implementation.

By the end of this lesson, you will have learned to:

• Implement a hybrid search approach, combining keyword- and semantic-search methods.
• Develop strategies for re-ranking search results to improve retrieval quality.
• Incorporate citation mechanisms to seamlessly track source information.

In the next section, we’ll kick things off by exploring response rankings.