Generative AI with Python

6    Retrieval-Augmented Generation

Sometimes it is the people no one imagines anything of who do the things no one can imagine.—Alan Turing in the movie The Imitation Game

In this chapter, we’ll cover one of the most impactful concepts in the field of large language models (LLMs): retrieval-augmented generation (RAG). So far, we have collected different puzzle pieces like LLMs, prompt engineering, and vector databases, which will now fit perfectly in place.

One important aspect is that vector databases, covered in Chapter 5, typically represent the backbone of a RAG system. For steering the model on how to create the final output, we’ll come back to prompt engineering and reuse the concepts we learned in Chapter 4. Finally, data will be passed ...