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Artificial intelligence has transformed many industries, including programming. The development of language models like GPT-3.5 and GPT-4 has made it possible for machines to generate code, an incredible advancement that was once thought impossible. In this article, we’ll dive into the world of coding with ChatGPT and GPT-4 and explore how AI is transforming the programming landscape.

What is ChatGPT & GPT-4?

ChatGPT & GPT-4 are artificial intelligence-based chatting platform that allows users to interact with a machine in a way that feels like a natural conversation. These are built in GPT-3.5/4 architectures, which are neural language models developed by OpenAI. GPT Models have been trained on a vast corpus of natural language data, which means that it can understand and respond to text-based inputs like a human. The platform uses advanced algorithms to understand the meaning behind text-based inputs and generates a response that’s appropriate to the context of the conversation.

Coding with ChatGPT and GPT-4

Coding with ChatGPT & GPT-4 is an exciting new area of artificial intelligence research. By using natural language inputs, developers can interact with ChatGPT/GPT-4 and generate code that meets their requirements. For example, a developer can describe the functionality they need in natural language, and ChatGPT will generate the corresponding code.

ChatGPT Coding Examples for Beginner/Advanced Programmers

Here are the examples of how a beginner or advanced developer might use ChatGPT/GPT-4 to generate code according to there requirements :

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Generating Simple Programs

ChatGPT can be used to generate simple programs with minimal effort. For example, a developer can request code to print the Fibonacci series:

Developer: “I need a program that prints the first ten numbers in the Fibonacci series in Python.”

ChatGPT: “Here’s the Python code you need to print the first ten numbers in the Fibonacci series:”

def fibonacci(n):
    if n <= 1:
        return n
        return fibonacci(n-1) + fibonacci(n-2)

for i in range(10):

Developing Complex Models

ChatGPT can generate code for more complex models, like neural networks or machine learning models. Here’s an example of how a developer can ask for a neural network that can recognize images of cars:

Developer: “I need a neural network that can recognize images of cars.”

ChatGPT: “Here’s the code you need to create a neural network that can recognize images of cars:”

import tensorflow as tf

model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(64, 64, 3)),
  tf.keras.layers.MaxPooling2D(2, 2),
  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(1, activation='sigmoid')

Creating RESTful APIs

ChatGPT can also generate code for building APIs. For example, a developer can request code for a RESTful API that can receive and store data in a database:

Developer: “I need a RESTful API that can receive and store data in a database.”

ChatGPT: “Here’s the code you need to create a RESTful API that can receive and store data in a database:”

from flask import Flask, jsonify, request
from flask_sqlalchemy import SQLAlchemy

app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///database.db'
db = SQLAlchemy(app)

class Data(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    name = db.Column(db.String(50))
    age = db.Column(db.Integer)
    email = db.Column(db.String(50))

@app.route('/data', methods=['POST'])
def add_data():
    name = request.json['name']
    age = request.json['age']
    email = request.json['email']
    new_data = Data(name=name, age=age, email=email)
    return jsonify

Generating Simple HTML/CSS code

ChatGPT can also generate HTML/CSS code for building web pages. Here’s an example of how a developer can ask for code to create a webpage with a navbar and a footer:

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Developer: “I need code to create a webpage with a navbar and a footer.”

ChatGPT: “Here’s the code you need to create a webpage with a navbar and a footer:”

<!DOCTYPE html>
	<title>Page Title</title>
		.navbar {
			overflow: hidden;
			background-color: #333;
			position: fixed;
			top: 0;
			width: 100%;

		.navbar a {
			float: left;
			display: block;
			color: white;
			text-align: center;
			padding: 14px 16px;
			text-decoration: none;
			font-size: 17px;

		.navbar a:hover {
			background-color: #ddd;
			color: black;

		.footer {
			background-color: #f2f2f2;
			padding: 10px;
			text-align: center;
			position: fixed;
			bottom: 0;
			width: 100%;

	<div class="navbar">
		<a href="#">Home</a>
		<a href="#">About</a>
		<a href="#">Contact</a>

	<div style="padding:20px;margin-top:30px;">
		<h2>Page Title</h2>
		<p>This is a sample page.</p>

	<div class="footer">


Code Refactoring

ChatGPT can also be used to refactor code. For example, a developer can ask ChatGPT to identify and replace duplicate code:

Developer: “Can you help me refactor this code and replace any duplicate code?”

ChatGPT: “Here’s the refactored code with duplicate code replaced:”

def print_factors(x):
   print("The factors of",x,"are:")
   for i in range(1, x + 1):
       if x % i == 0:

num = 320


Limitation of Coding with GPT-3.5/4 Models

While ChatGPT is an impressive tool for generating code, it does have some limitations. First, the code generated by ChatGPT is not always optimized for performance or efficiency. Developers may need to refine the code generated by ChatGPT to meet their specific requirements. Additionally, ChatGPT may not always understand the developer’s intent or generate the correct code.

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Coding with ChatGPT: Conclusion

ChatGPT is an incredible advancement in the world of programming. It has the potential to save developers a lot of time and effort by generating code for simple and complex applications, APIs, webpages, and even refactoring code. It’s important to note that ChatGPT is not a replacement for human developers, but rather a tool that can aid in the development process. By leveraging the power of AI and machine learning, developers can create applications more efficiently and effectively.


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