5 Tips to Easily Improve Your Coding Skills with ChatGPT

Anyone who has spent a few minutes spinning off ChatGPT will understand the potential, as well as the concerns such technology, yields when it comes to work. (especially White-Collar Jobs) The notorious chatbot is able to answer all manner of queries—from writing captions for your next social media post to coding problems to history or legal conundrums—with remarkable effectiveness.

It won’t be an exaggeration to mention that ChatGPT and its related tools have made AI available to the masses. With a short and right prompt, these bots can come up with sonnets, code, philosophy, thesis, and more. With context to software development, ChatGPT recently passed a Google coding challenge. So basically, AI practically got a job at one of the FAANG companies, infact at one of its biggest rivals. So the main question that arises is if ChatGPT will replace developers. And as a professional, should we be concerned?

On the contrary, ChatGPT is only as capable as someone fine-tuning it, with their years of domain-specific knowledge. Instead, we shall embrace this technology. In this detailed article, we’re going to unveil some creative tips and prompts to easily improve your coding skills with ChatGPT.

We’re also going to take a sneak peek into “What is Prompt Engineering” and how you can use these time-saving creative prompts to demo techniques, write small algorithms, subroutines, and more. So let’s get started.

Improve Your Coding Skills with ChatGPT

In the context of developers and coders, ChatGPT refers to a robust language model developed by OpenAI. It is specifically designed to assist programmers in various aspects of their work, making it easier for them to write code with ChatGPT, generate code snippets, document their code, and seek guidance on programming concepts and best practices.

ChatGPT is trained on a vast amount of data from the internet, allowing it to understand and generate human-like text. This training data includes a wide range of programming languages, libraries, frameworks, and coding styles, enabling ChatGPT to provide accurate and context-aware responses related to coding tasks.

Before we embark on our journey, it’s essential for you to grasp the programming language before placing unwavering trust in any information provided by ChatGPT. We emphasize this point because there have been instances where people have been highly misinformed, considering ChatGPT programming as the ultimate solution while dismissing the importance of human developers.

Employing ChatGPT or similar Language Models doesn’t automatically grant you expertise, nor does it instantly enhance your productivity. Indiscriminately copying and pasting code is counterproductive; it reflects laziness rather than ingenuity. This practice can lead to unforeseen consequences that may directly or indirectly harm your ongoing projects if you incorporate code without understanding its implications.

It is crucial to acknowledge that ChatGPT has been trained on publicly available data predating 2021. Consequently, its results may exhibit biases, errors, and a lack of citations. Therefore, it is imperative to always bear in mind that its ability to provide accurate and up-to-date answers is limited by the accuracy and relevance of its training datasets. However, OpenAI is continuously working on methods to ensure that models remain current, and this may improve with time.

Having Said That, let’s jump in, and look at some valuable tips that will help you augment your code better and teach you how to work with ChatGPT.

1. Code Generation

To tick off first in the list, ChatGPT is capable of generating quick code with a high level of accuracy. developers can provide a description or prompt, and ChatGPT can generate functional code snippets based on that input. This capability is particularly useful for quickly prototyping solutions, exploring different coding approaches, or tackling complex coding problems. With code generation, ChatGPT becomes a valuable tool for developers looking to expedite their coding process.

# Let's say we have a list of strings.
fruits = ['apple', 'banana', 'orange']

# We want to iterate over the list and print each fruit.
for fruit in fruits:
    print(fruit)

We prompted ChatGPT to print each fruit, which came up with this response.

The initial code prompts ChatGPT to iterate over a list of fruits and print each fruit using a for loop. After the completion, ChatGPT suggests adding a condition to only print fruits starting with the letter ‘a’ using the startswith() method. It understands the context of the code and offers intelligent suggestions to enhance the functionality. However, in this example, we want to convey the print command via just a single line of code rather than writing the command startswith() three times. So We went back to ChatGPT and asked them to iterate again.

So now we know what’s the difference between these two functions, if we’re not sure about some coding function, we can always ask ChatGPT, our recent work companion.

2. Code Completion.

One of the primary uses of ChatGPT for developers is code completion. As programmers write code, ChatGPT can predict and suggest the next lines of code, saving time and reducing the effort required for manual typing. By offering intelligent code completion suggestions, ChatGPT enhances the coding workflow and assists developers in writing code more efficiently.

// Let's say we have an array of numbers.
const numbers = [1, 2, 3, 4, 5];

// We want to iterate over the array and multiply each number by 2.
numbers.forEach((number) => {
  const multipliedNumber = number * 2;
  console.log(multipliedNumber);
});

For this problem, we asked ChatGPT, and they generated this response.

The initial code prompts ChatGPT to iterate over an array of numbers and multiply each number by 2 using the forEach method. After the completion, ChatGPT suggests adding a condition to only multiply even numbers by 2. It recognizes the context of the code and provides intelligent suggestions to enhance the functionality.

Code completion with ChatGPT can save developers time by offering relevant suggestions based on the code they’ve already written, helping them explore different possibilities and improve their code efficiently.

Let’s try ChatGPT code completion for a complex problem.

# Let's say we have a dictionary representing a student's grades in different subjects.
student_grades = {
    'math': 90,
    'science': 85,
    'history': 95
}

# We want to calculate the average grade for the student.

Now we shall ask ChatGPT for code completion.

# Let's say we have a dictionary representing a student's grades in different subjects.
student_grades = {
    'math': 90,
    'science': 85,
    'history': 95
}

# We want to calculate the average grade for the student.

# Code completion suggestion:
# Let's calculate the average grade by summing the grades and dividing by the number of subjects.
grades_sum = sum(student_grades.values())
num_subjects = len(student_grades)
average_grade = grades_sum / num_subjects

print(f"The average grade is: {average_grade}")

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3. Code Documentation

ChatGPT also excels in code documentation. It can generate descriptive comments and explanations for code, making it easier for developers to understand their own code or for others to collaborate effectively. Proper code documentation is crucial for maintaining codebases and facilitating teamwork, and ChatGPT’s ability to generate clear and concise documentation is a significant benefit for developers. ChatGPT can also scan through your source code including README.md files for projects.

Make sure you add:

• A prompt with the code function snippet you wish to document & add enough context.
• More Details if requested.

Imagine you are working on a collaborative project with a team of developers. One of your colleagues has written a complex function to calculate the Fibonacci sequence, but the code lacks proper documentation.

def fibonacci(n):
    fib_sequence = [0, 1]
    
    while len(fib_sequence) < n + 1:
        next_number = fib_sequence[-1] + fib_sequence[-2]
        fib_sequence.append(next_number)
    
    return fib_sequence

In the initial code snippet, the function fibonacci() is present, but it lacks any documentation to explain its purpose, input parameters, or return value. Without proper documentation, it becomes difficult for other developers to understand the intention and functionality of the code. Clear and concise documentation is crucial for maintaining codebases and collaborating effectively with team members.

You decide to step in and improve the documentation to make the code more understandable and accessible to everyone involved.

Here’s an example of how code documentation using ChatGPT can greatly enhance the understanding of the Fibonacci calculation function:

def fibonacci(n):
    """
    Calculate the Fibonacci sequence up to the given number.
    
    Parameters:
    - n (int): The number up to which the Fibonacci sequence will be calculated.
    
    Returns:
    - fib_sequence (list): A list containing the Fibonacci sequence up to the given number.
    """
    fib_sequence = [0, 1]  # Initialize the sequence with the first two numbers
    
    while len(fib_sequence) < n + 1:
        next_number = fib_sequence[-1] + fib_sequence[-2]
        fib_sequence.append(next_number)
    
    return fib_sequence

In this scenario, the code documentation has been significantly improved. The function now includes a docstring, which is a multi-line comment enclosed in triple quotes. The docstring provides essential information about the function, its purpose, parameters, and return value.

The docstring explains that the fibonacci() function calculates the Fibonacci sequence up to the given number (n). It clarifies that the function expects an integer value as the input parameter.

Additionally, the docstring describes the return value of the function, which is a list (fib_sequence) containing the Fibonacci sequence up to the given number. This information helps other developers understand what they can expect when using this function.

With this improved code documentation, any team member who reads this function can quickly understand its purpose, the expected input, and the resulting output. This documentation saves time and effort that would otherwise be spent on deciphering the code’s functionality. It also aids in maintaining and debugging the code in the future.

Brad vs ChatGPT: Which ChatBot is Better for Software Development?

4. Styling Output

ChatGPT can assist developers in styling their outputs by providing suggestions and examples for formatting, layout, and presentation. Here’s how ChatGPT helps in styling outputs efficiently:

Formatting suggestions

When developers need to present their output in a specific format, such as date formatting, currency formatting, or string manipulation, they can ask ChatGPT for suggestions. ChatGPT can generate code snippets or recommend built-in functions and libraries that can help achieve the desired formatting.

import datetime

# Let's say we have a date and we want to format it as "YYYY-MM-DD".
date = datetime.date(2023, 5, 18)

# Ask ChatGPT for a formatting suggestion
formatted_date = date.strftime("%Y-%m-%d")

print(formatted_date)

//OUTPUT

2023-05-18

Visual Representation

ChatGPT can provide guidance on visualizing data by suggesting appropriate charting libraries, techniques, or even code snippets for creating charts, graphs, or other visual representations. Whether it’s plotting data points, creating histograms, or generating interactive visualizations, ChatGPT can offer insights to style and present data in an aesthetically pleasing manner.

import matplotlib.pyplot as plt

# Let's say we have a list of values representing sales data for different months.
months = ['Jan', 'Feb', 'Mar', 'Apr']
sales = [5000, 6500, 4800, 7000]

# Ask ChatGPT for a visualization suggestion
plt.bar(months, sales)
plt.xlabel('Months')
plt.ylabel('Sales')
plt.title('Monthly Sales Data')
plt.show()

As an output, we’ll have a bar chart displaying the monthly sale data.

Styling text and output

When it comes to styling text outputs, such as applying colors, and font styles, or highlighting specific sections, ChatGPT can provide recommendations on the relevant libraries or functions to achieve the desired styling. It can suggest HTML tags, CSS properties, or even specific code snippets to format and style text outputs effectively.

# Let's say we have a string and we want to style it by applying bold formatting.
text = "Hello, world!"

# Ask ChatGPT for a styling suggestion
styled_text = f"<b>{text}</b>"

print(styled_text)

Hello, world 

In this example, ChatGPT suggests wrapping the text with HTML tags <b> and </b> to apply bold formatting. The styled text can be used in various contexts, such as generating HTML output or displaying formatted text in specific environments.

Layout and organization

ChatGPT can offer suggestions on how to structure and organize outputs to enhance readability and user experience. Whether it’s arranging data in tables, creating sections and subsections, or implementing pagination, ChatGPT can provide ideas and best practices to optimize the layout and organization of outputs.

# Let's say we have a list of items and we want to organize them in a table format.
items = [
    {'name': 'Item 1', 'price': 10},
    {'name': 'Item 2', 'price': 15},
    {'name': 'Item 3', 'price': 20}
]

# Ask ChatGPT for a layout suggestion
table = "\n".join([f"{item['name']:10} {item['price']:10.2f}" for item in items])

print(table)

Item 1 10.00
Item 2 15.00
Item 3 20.00

ChatGPT suggests using a list comprehension to generate a formatted table with aligned columns. The join() method is used to combine the rows of the table with newline characters.

Responsive design

In scenarios where outputs need to adapt to different screen sizes or platforms, ChatGPT can guide developers on responsive design techniques and frameworks. It can suggest CSS media queries, responsive grid systems, or mobile-first development approaches to ensure the output looks and functions well across various devices and screen sizes. For example:

<!-- Let's say we have a responsive web page and we want to adjust the layout for mobile devices. -->
<!DOCTYPE html>
<html>
<head>
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <style>
        .container {
            display: grid;
            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
            gap: 10px;
        }
        
        .item {
            background-color: #f2f2f2;
            padding: 10px;
            text-align: center;
        }
    </style>
</head>
<body>
    <div class="container">
        <div class="item">Item 1</div>
        <div class="item">Item 2</div>
        <div class="item">Item 3</div>
    </div>
</body>
</html>

ChatGPT suggests using CSS media queries and a grid layout to create a responsive design for a web page. The grid layout adjusts the number of columns based on the available screen width, and the media query (width=device-width) ensures that the page scales properly on different devices.

5. Creating Test Cases

It’s obvious, that we can’t rely on ChatGPT 100% while writing code, however, when it comes to writing test cases, you can place your trust in the chatbot as a source of inspiration. For one reason, that test cases are not part of the developer’s parcel code, the risk associated with leveraging ChatGPT is negligible. By providing specific inputs, requirements, or scenarios, developers can ask ChatGPT to generate test case ideas or outline the steps to be followed for testing.

A DevOps engineer might ask ChatGPT to generate test cases for login functionality. By specifying different scenarios, such as valid credentials, invalid passwords, or edge cases, ChatGPT can suggest possible test cases to cover various scenarios. It can provide test case templates, data input suggestions, or even outline the expected results for each test case.

Imagine you are a DevOps engineer responsible for testing a SaaS (Software as a Service) application that provides a subscription management system. As part of your testing efforts, you need to create test cases to ensure the functionality of the login feature.

Here’s an example of how ChatGPT can assist you in generating test cases for the login functionality. (Code in Visual Basic)

DevOps Engineer: Hi ChatGPT, I need assistance in generating test cases for the login functionality of our SaaS application. Can you help me with that?
ChatGPT: Of course! I'd be happy to help. Could you provide me with some information about the login feature? For instance, what are the input fields, expected outcomes, and any specific scenarios you want to cover?
DevOps Engineer: Sure. The login feature consists of two input fields: email and password. Upon successful login, the user should be directed to the dashboard. If the login fails, appropriate error messages should be displayed.
ChatGPT: Great! Based on that, here are a few test cases that you can consider:

1. Test Case: Valid Login
   - Input: Valid email and password
   - Expected Outcome: User is successfully logged in and directed to the dashboard.

2. Test Case: Invalid Email
   - Input: Invalid email and valid password
   - Expected Outcome: Appropriate error message is displayed, indicating that the email is invalid.

3. Test Case: Invalid Password
   - Input: Valid email and invalid password
   - Expected Outcome: Appropriate error message is displayed, indicating that the password is incorrect.

4. Test Case: Empty Fields
   - Input: Empty email and password fields
   - Expected Outcome: Appropriate error messages are displayed, indicating that both fields are required.

5. Test Case: Account Lockout
   - Input: Entering incorrect password multiple times
   - Expected Outcome: After a certain number of failed login attempts, the account is locked and the user is notified.

Please note that these are just a few examples. You can expand and customize these test cases based on your specific requirements and scenarios.

You can also format these test cases for Wiki, JIRA, or GitHub Tickets. For example, we prompted ChatGPT to create a formatted table to use in JIRA.

By leveraging ChatGPT’s language generation capabilities, DevOps engineers and QA’s can quickly generate test cases, saving time and effort in the testing process. The suggestions provided by ChatGPT can serve as a starting point for creating comprehensive test suites, ensuring better coverage and quality in software testing.

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In Closing?

Whether you’re a seasoned programmer or just starting out on your coding journey, finding effective ways to improve your coding skills with ChatGPT is crucial for staying relevant and competitive in the digital age. Fortunately, with the advancements in artificial intelligence (AI), we now have a powerful tool at our disposal to enhance our coding abilities: ChatGPT.

Whether to use ChatGPT or not, it’s entirely up to you. Although it’s indubitable quicker to prompt ChatGPT to generate what you need, and later review it compared to writing everything from scratch. Additionally, AI and ChatGPT tools can also help you learn a new programming language or functionality in a seamless & interactive way. Regardless of your decision, one thing is certain — AI is here to stay and we’ll be hearing a lot about it.

There’s a certain feeling that happens when a new technology adjusts your thinking about computing. Google did it. Firefox did it. AWS did it. iPhone did it. OpenAI is doing it with ChatGPT.

Aaron Levie, CEO & Co-founder of BOX

And yes, even from us. :p We’ll be adding more blogs to our ChatGPT series, so stay tuned.

FAQs

Are AI tools like ChatGPT expected to replace human developers in the coding industry?

No, AI tools like ChatGPT are not intended to replace human developers. Instead, they are designed to assist and augment human capabilities. While AI can automate certain repetitive tasks and provide suggestions, human developers bring creativity, problem-solving abilities, and domain expertise to the development process. The future of coding is likely to involve a symbiotic relationship between humans and AI, where developers leverage AI tools to enhance their productivity and efficiency.

What impact will AI-powered coding tools, such as ChatGPT, have on software development workflows?

AI-powered coding tools are expected to streamline and optimize software development workflows. They can accelerate coding tasks, provide intelligent code completion, assist with debugging, generate test cases, and offer code review suggestions. These tools can help developers write higher-quality code, increase productivity, and reduce time spent on repetitive or mundane tasks. As a result, software development workflows are likely to become more efficient and productive.

With the rise of AI coding tools, what skills should developers focus on developing?

Developers should focus on skills that complement AI coding tools. These skills include problem-solving, algorithm design, critical thinking, system architecture, and understanding complex business requirements. Developers should also focus on domain-specific knowledge, as AI tools require guidance and expertise from human developers to be effective. Additionally, soft skills like collaboration, communication, and adaptability will continue to be valuable in a future where AI and human developers work together to create innovative solutions.