1 Introduction to the use of Generative AI in Data Analytics
This chapter covers
- The introduction to key limitations of Generative AI models
- Role of Generative AI in Data Analytics
- Getting started with using LLMs to support Data Analytics
As the dust over Generative AI hype begins to settle and the notes of disappointment mix into the chorus of praises, it may be a good time to ask yourself a question. If LLMs aren’t the silver bullet to all world problems, what are they really good for? Our experience using these amazing tools to improve various processes gave us the answer. They are really good, and we mean really good in supporting improvements of different processes. Throughout this book, we will guide you through our methods for utilizing the enormous potential hidden in matrices of Generative AI to improve your analytics skills without falling victim to risks inherent to this technology.
Under the hood
To excel in both tasks, you would preferably have in the back of your head what drives the responses you get to your prompts. However, due to the architecture-agnostic nature of this book and the rapidly changing technology landscape, we consciously avoid the technological nitty-gritty, focusing instead on process implementation. We encourage you, though, to get a good overview of what’s-what. You can learn it from, e.g., The Complete Obsolete Guide to Generative AI or Introduction to Generative AI, and for technical details of GPT models: How GPT works.