Part 2: Designing

This part focuses on the most technical part of LLM design, the work done in the LLM, or when using other tools and techniques to make an end-to-end LLM solution. We will start by introducing practices such as Retrieval Augmented Generation (RAG) to integrate enterprise datasets with the generative ability of an LLM. You’ll then learn about the fundamentals of prompt engineering for enterprise applications. The examples follow a non-technical route, so you can get the most out of learning about the steps involved without coding. We’ll then explore fine-tuning to make models think and act based on examples provided that will follow the style and tone needed for any business or enterprise. We’ll also explore a few case studies and give some hands-on experiences to get a feel for the process.

This part includes the following chapters:

• Chapter 6, Gathering Data – Content Is King
• Chapter 7, Prompt Engineering
• Chapter...