In this chapter, we introduced LlamaIndex, a framework for connecting LLMs to external datasets. We discovered how LlamaIndex allows LLMs to incorporate real-world knowledge into their responses.

The chapter discussed the benefits of LlamaIndex over fine-tuning, such as easier updating and personalization. It introduced the concept of progressive disclosure of complexity, where LlamaIndex starts simple but reveals advanced capabilities when needed.

The chapter then presented an overview of the hands-on project PITS, a personalized intelligent tutoring system. It covered setting up the required tools such as Python, Git, and Streamlit, and getting an OpenAI API key. The chapter finished by verifying that the environment is ready for building LlamaIndex apps.

We’re now ready to continue our journey and proceed with a more technical understanding of the inner workings of the LlamaIndex framework. See you in the next chapter!