Deploying and Exploring OpenAI in Azure

In this chapter, we will be deploying Azure OpenAI and interacting with the different LLM models. Azure also provides a user interface (UI) to experiment with the LLMs which we will explore. We will also be introducing additional parameters that can be used to influence the behavior of the LLM, such as Temperature, Top-p, Frequency penalty, and Presence penalty. This will prepare us for Chapter 4 , where we will learn how to ground the LLM with your own data before proceeding to architect a full-blown enterprise LLM solution based on the Retrieval Augmented Generation (RAG) architecture.