Developing the Future with ChatGPT

In this chapter, we will discuss how developers can leverage ChatGPT. The chapter focuses on the main use cases ChatGPT addresses in the domain of developers, including code review and optimization, documentation generation, and code generation. The chapter will provide examples and enable you to try the prompts on your own.

After a general introduction about the reasons why developers should leverage ChatGPT as a daily assistant, we will focus on ChatGPT and how it can do the following:

• Why ChatGPT for developers?
• Generate, optimize, and debug code
• Generate code-related documentation and debug your code
• Explain machine learning (ML) models to help data scientists and business users with model interpretability
• Translate different programming languages

By the end of this chapter, you will be able to leverage ChatGPT for coding activities and use it as an assistant for your coding productivity.

Why ChatGPT for developers?

Personally, I believe that one of the most mind-blowing capabilities of ChatGPT is that of dealing with code. Of any type. We’ve already seen in Chapter 4 how ChatGPT can act as a Python console. However, ChatGPT capabilities for developers go way beyond that example. It can be a daily assistant for code generation, explanation, and debugging.

Among the most popular languages, we can certainly mention Python, JavaScript, SQL, and C#. However, ChatGPT covers a wide range of languages, as disclosed by itself:

Figure 6.1 – ChatGPT lists the programming languages it is able to understand and generate

Whether you are a backend/frontend developer, a data scientist, or a data engineer, whenever you work with a programming language, ChatGPT can be a game changer, and we will see how in the several examples in the next sections.

From the next section onward, we will dive deeper into concrete examples of what ChatGPT...

Generating, optimizing, and debugging code

The primary capability you should leverage is ChatGPT code generation. How many times have you been looking for a pre-built piece of code to start from? Generating the utils functions, sample datasets, SQL schemas, and so on? ChatGPT is able to generate code based on input in natural language:

Figure 6.2 – Example of ChatGPT generating a Python function to write into CSV files

As you can see, not only was ChatGPT able to generate the function, but also it was able to explain what the function does, how to use it, and what to substitute with generic placeholders such as my_folder.

Another example could be of creating schemas for structured tables. Namely, imagine you are a data engineer in the process of creating relationships between two standard tables in an SAP ERP system: MARA and VBAP, storing, respectively, general material data and sales document data.

Let’s ask ChatGPT to create the...

Generating documentation and code explainability

Whenever working with new applications or projects, it is always good practice to correlate your code with documentation. It might be in the form of a docstring that you can embed in your functions or classes so that others can invoke them directly in the development environment.

For example, the following Python class has 10 different methods for basic mathematical operations:

class Calculator:
    def add(self, x, y):
        return x + y
    def subtract(self, x, y):
        return x - y
    def multiply(self, x, y):
        return x * y
    def divide(self, x, y):
        try:
            return x / y
     ...

Understanding ML model interpretability

Model interpretability refers to the degree of ease with which a human can comprehend the logic behind the ML model’s predictions. Essentially, it is the capability to comprehend how a model arrives at its decisions and which variables are contributing to its forecasts.

Let’s see an example of model interpretability using a deep learning convolutional neural network (CNN) for image classification. I built my model in Python using Keras. For this purpose, I will download the CIFAR-10 dataset directly from keras.datasets: it consists of 60,000 32x32 color images (so 3-channels images) in 10 classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck), with 6,000 images per class. Here, I will share just the body of the model; you can find all the related code in the book’s GitHub repository for data preparation and pre-processing at https://github.com/PacktPublishing/Modern-Generative-AI-with-ChatGPT...

Translation among different programming languages

In Chapter 5, we saw how ChatGPT has great capabilities for translating between different languages. What is really incredible is that natural language is not its only object of translation. In fact, ChatGPT is capable of translating between different programming languages while keeping the same output as well as the same style (namely, it preserves docstring documentation if present).

There are so many scenarios when this could be a game changer.

For example, you might have to learn a new programming language or statistical tool you’ve never seen before because you need to quickly deliver a project on it. With the help of ChatGPT, you can start programming in your language of preference and then ask it to translate to the desired language, which you will be learning alongside the translation process.

Imagine that the project needs to be delivered in MATLAB (a proprietary numerical computing and programming software...

Summary

ChatGPT can be a valuable resource for developers looking to enhance their skills and streamline their workflows. We started by seeing how ChatGPT can generate, optimize, and debug your code, but we also covered further capabilities such as generating documentation alongside your code, explaining your ML models, and translating between different programming languages for application modernization.

Whether you’re a seasoned developer or just starting out, ChatGPT offers a powerful tool for learning and growth, reducing the gap between code and natural language.

In the next chapter, we will dive deeper into another domain of application where ChatGPT could be a game changer: marketing.