11 Contextualizing prompts with retrieval augmented generation
This chapter covers
- Outlining how retrieval augmented generation (RAG) works
- Using tooling to create a basic RAG setup
- Integrating vector databases into a RAG setup
As we learned in the previous chapter, one of the challenges of working with LLMs is that they lack visibility of our context. During part 2 of this book, we looked at different ways in which we can arrange our prompts to help provide small insights into our context. However, these types of prompts only have a certain amount of mileage before the lack of extra context leads to less valuable responses. Therefore, to increase the value of an LLM’s response, it stands to reason that we need to provide more contextual detail into our prompt. In this chapter, we’ll explore how we can do this through retrieval augmented generation, or RAG. We’ll learn how RAG works, why it’s of value to us, and how it’s not a big jump from prompt engineering to building our own RAG framework examples to establish our understanding of how they can help us in a testing context.