- 1.1 Introduction
- 1.2 Chapter contents
- 1.3 Communication and neuro linguistic programming (nlp)
- 1.4 Abstraction
- 1.5 Finding the right level of abstraction for Generative AI
- 1.6 Choice of Generative AI
- 1.7 Applying Generative AI to an example problem domain
- 1.8 Modeling in Generative Analysis
- 1.9 Chapter summary
This chapter is from the book
This chapter is from the book
This chapter is from the book
1.5 Finding the right level of abstraction for Generative AI
By now, you might be wondering why we’re making so much fuss about abstraction. The answer is that we will demonstrate that this is the essential skill needed to thrive in a software engineering industry that may soon come to be dominated by Generative AI. Let us convince you by considering a simple example.
We will use a Generative AI to generate some Python code to solve a simple business problem. The input to the AI will be a textual description of the system.
It is important to note that we are doing this in May 2023. Whenever you are reading this, we want you to follow along with our example using your choice of Generative AI. It is likely to be more advanced, or at least different, so you should expect results that are similar but not necessarily identical to ours. Nevertheless, we expect that our arguments about abstraction will still hold, unless things move a lot faster than we think and AGI has been achieved. In which case, join us in welcoming our new AI Overlords.
