How to Use Generative AI for Coding: The Ultimate Guide for Every Programmer
November 7th, 2024 by Rojesh Shikhrakar
As developers, we navigate complex requirements, turning them into functioning solutions that live in code. But writing code is only part of the journey. There’s also debugging, optimizing, testing, and maintaining it—all part of coding’s daily challenges.
Over the years, we’ve built a toolkit to help manage these tasks: IDEs, debuggers, compilers, and tools like Microsoft IntelliSense, Kite, and Tabnine. But what if I told you that Generative AI could be even more powerful and efficient and act as your new mighty assistant?
In this article, we’ll explore how you can leverage generative AI in your coding workflow, covering:
- GenAI Tools for Code
- Automated Code Generation
- AI-Assisted Code Completion
- Debugging with AI
- Code Optimization and Refactoring
- Writing Automated Tests
- Documentation of Your Code with AI
- Understanding Code
- Learning New Programming Languages or Frameworks
- Advanced: AI Agents for Coding
- Ethical Considerations
Ready to dive in? Let’s go !
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GenAI tools for Code
But before we begin, here are some Gen AI tools you can use to get started with pair programming with AI:
- Replit: AI-driven platform that integrates the power of AI with a collaborative coding environment. It features Ghostwriter, an AI assistant that offers real-time code suggestions, completions, and enhancements.
- GitHub Copilot: Based on OpenAI's GPT models, GitHub Copilot offers code completion and suggestions in Visual Studio Code.
- Sourcegraph Cody: Popular tool with multiple LLMs and advanced code search and analysis capabilities to enhance developers' understanding of code and generation.
- Tabnine: Uses AI to complete code snippets across various programming languages, boosting productivity.
These tools use large language models (LLMs) like OpenAI GPT-4, Anthropic's Claude, Mistral AI, Google Gemini, Meta Llama, and others to generate code, debug, refactor, and much more.
While AI can significantly improve coding efficiency, think of it as a collaborative partner, not a replacement. Approach it as a tool to augment your skills and insights, keeping expectations grounded.
1. Automated Code Generation
One of the most powerful applications of generative AI in coding is automated code generation. AI can generate anything from snippets to entire functions, saving time on repetitive tasks.
Example 1: CRUD application
For example, when building a web application with a database, you usually need to create CRUD (Create, Read, Update, Delete) operations. Instead of manually writing this boilerplate code, AI can generate it for you.
Prompt
AI Output
#Define the User Model:
from django.db import models
class User(models.Model):
username = models.CharField(max_length=100)
email = models.EmailField(unique=True)
first_name = models.CharField(max_length=50)
last_name = models.CharField(max_length=50)
def __str__(self):
return self.username
#Create Views for CRUD Operations:
from django.shortcuts import render, get_object_or_404, redirect
from .models import User
from .forms import UserForm
# Create
def create_user(request):
if request.method == 'POST':
form = UserForm(request.POST)
if form.is_valid():
form.save()
return redirect('user_list')
else:
form = UserForm()
return render(request, 'user_form.html', {'form': form})
# Read
def user_list(request):
users = User.objects.all()
return render(request, 'user_list.html', {'users': users})
def user_detail(request, pk):
user = get_object_or_404(User, pk=pk)
return render(request, 'user_detail.html', {'user': user})
# Update
def update_user(request, pk):
user = get_object_or_404(User, pk=pk)
if request.method == 'POST':
form = UserForm(request.POST, instance=user)
if form.is_valid():
form.save()
return redirect('user_detail', pk=user.pk)
else:
form = UserForm(instance=user)
return render(request, 'user_form.html', {'form': form})
# Delete
def delete_user(request, pk):
user = get_object_or_404(User, pk=pk)
if request.method == 'POST':
user.delete()
return redirect('user_list')
return render(request, 'user_confirm_delete.html', {'user': user})
#Create Forms for User Model:
….
#Creates Templates html files ….
#Configure URLs …
AI-generated code can be a huge time-saver, letting you focus on unique business logic rather than standard setup. Most models and tools understand the requirements for the frameworks very well, allowing them to create the different files, classes, and functions needed.
Example 2: Generating UI
You can use tools such as v0 by Vercel to generate the front end for your applications by incorporating your requirements in your prompts:
Prompt
Create a secure and user-friendly login page with React with the following features: Username/Email Field, Password Field, Remember Me Option, Forgot Password Link, Clear and concise error messages for invalid login attempts, social login options, Captcha, …
This prompt creates a UI and associated code that can be easily customized and integrated with other parts of the code for different functionalities.
2. AI-Assisted Code Completion
You might want to have better control over your code by using AI to complete only a few lines of code rather than creating the entire section of code. Most tools like GitHub Copilot, Sourcegraph Cody, and Cursor use AI to analyze your current code and provide context-aware suggestions, helping you complete lines of code or entire functions. You can write a prompt describing the code. However, most IDEs are already equipped with technology to trigger code completion that looks like IntelliSense in Visual Studio Code or triggers using shortcut keys.
With AI’s assistance, you can finish coding faster and gain valuable insights into best practices for your code.
3. Debugging with AI
There’s a famous quote about debugging: “90% of coding is debugging. The other 10% is writing bugs". Some say, “Coding is 90% debugging and 10% cursing at your computer.“ Debugging is the most time-consuming task for both beginners and experienced. The days of banging your head against the keyboard to figure out why your code isn’t working are ending with the help of AI. Generative AI tools can help you spot errors, suggest fixes, and even explain what went wrong (because, let’s face it, sometimes you just don’t know why your code hates you).
Debugging is one of the most challenging aspects of coding, but AI-powered tools can help make this process much faster. Generative AI models can detect errors in your code, suggest possible fixes, and even explain why the bug exists. By analyzing your code in real-time, these AI systems reduce the time developers spend hunting down bugs and improve the overall quality of the code.
For example, if you encounter a TypeError in Python, you can ask AI to review the code and provide a solution.
Prompt
4. Code Optimization and Refactoring
Refactoring involves restructuring existing code without changing its external behavior, often to improve performance, readability, or maintainability. AI can analyze your code, identify potential bottlenecks, and suggest changes that enhance performance. It can help remove unnecessary loops, improve data structures, and ensure your code is readable.
For example, you can optimize a function that iterates over a large dataset to improve efficiency.
Prompt
AI Output
# Original code
def sum_large_list(numbers):
total = 0
for num in numbers:
total += num
return total
# Optimised Code using Python's built-in sum function
def sum_large_list(numbers):
return sum(numbers)
AI suggests a more efficient approach to improving performance by replacing a manual loop with Python's built-in sum() function.
Generative AI can profile code to identify slow or inefficient sections, recommending optimizations that enhance overall application performance. This proactive approach to code quality helps teams maintain a competitive edge.
Prompt
#original code
def calculate_sum(n):
total = 0
for i in range(n):
total += i
return total
result = calculate_sum(1000000)
print(result)
The code is functioning correctly but could be optimized. Notably, the for loop iterates over all values from 0 to n-1 to sum the numbers. This can be replaced with a mathematical formula for the sum of the first n integers, which is much more efficient. Here’s an optimized version of the code:
def calculate_sum(n):
return n * (n - 1) // 2
result = calculate_sum(1000000)
print(result) # Output: 499999500000
This reduces the time complexity from O(n) to O(1), making it much faster for larger values of n.
5. Writing Automated Tests
Testing is a critical part of software development, but most dislike writing one. AI can assist with automatically generating unit tests based on the code provided. AI can also help document your code, making it easier for other developers to understand the logic and flow of your application. Both tests and documentation are essential for maintaining high-quality codebases, especially in large projects.
Prompt
import unittest
class TestFibonacci(unittest.TestCase):
def test_fibonacci_single_element(self):
self.assertEqual(fibonacci(1), [0])
def test_fibonacci_two_elements(self):
self.assertEqual(fibonacci(2), [0, 1])
def test_fibonacci_multiple_elements(self):
self.assertEqual(fibonacci(5), [0, 1, 1, 2, 3])
def test_fibonacci_invalid_input(self):
self.assertEqual(fibonacci(-1), "Input should be a positive integer.")
if __name__ == '__main__':
unittest.main()
In this case, AI generates a basic set of unit tests to ensure the Fibonacci function behaves correctly under various conditions, helping you save time on test creation.
6. Documentation of your code with AI
A code is read much more often than written. But an uncommented code can easily be a puzzle for both your future self and other developers. Like code completion, AI can generate comments, docstrings, and different types of documentation. This means no more cryptic comments like “// TODO: figure this out later.” AI can take in the code context, make sense of your code, and even explain it like you're five.
def generate_correlated_data(n, corr, mu_x=0, sigma_x=1, mu_y=0, sigma_y=1):
x = np.random.normal(mu_x, sigma_x, n)
y = corr * x + np.random.normal(mu_y, sigma_y, n) * np.sqrt(1 - corr**2)
return x, y
Alt + D to generate documentation for the code block in Sourcegraph Cody. Alternatively, you can ask any LLM to generate doc strings for the code.
Write a doc string for the following Python code …
def generate_correlated_data(n, corr, mu_x=0, sigma_x=1, mu_y=0, sigma_y=1):
"""
Generate a pair of correlated data points with a specified correlation coefficient.
Args:
n (int): Number of data points to generate.
corr (float): Correlation coefficient between x and y data.
mu_x (float, optional): Mean of x distribution. Defaults to 0.
sigma_x (float, optional): Standard deviation of x distribution. Defaults to 1.
mu_y (float, optional): Mean of y distribution. Defaults to 0.
sigma_y (float, optional): Standard deviation of y distribution. Defaults to 1.
Returns:
Tuple: Two 1D NumPy arrays (x, y) of length n with specified correlation.
"""
x = np.random.normal(mu_x, sigma_x, n)
y = corr * x + np.random.normal(mu_y, sigma_y, n) * np.sqrt(1 - corr**2)
return x, y
7. Understanding Code
Understanding why your code works or doesn’t work can sometimes puzzle most beginners. AI can act like the code whisperer, breaking down confusing logic, explaining complex functions, and deciphering those thousand-line methods without beginning or end. Instead of painstakingly tracing each variable through layers of if-statements, loops, and the occasional black hole, AI can summarize what’s happening and why. You can simply ask any LLM using the prompt.
Prompt
However, as the code base becomes more significant in size, it may be challenging to pass the entire code into the LLM. In such scenarios, coding tools/assistants such as cursor Cody can capture the context of a whole codebase and explain specific code segments. The entire codebase is indexed and analyzed by the tool through an Abstract Syntax Tree (AST) representing the entire code base as a tree along with static analysis of the dependencies, variables in scopes, and function definitions to help the model understand how different parts of the code interact.
8. Learning New Programming Languages or Frameworks
Generative AI can be an excellent tutor when learning a new programming language or framework. It can generate code snippets in various languages and explain the differences between them. This capability is handy when translating code from one language to another or trying to understand how a particular framework operates compared to others.
Prompt
Sometimes, your coding needs are a little...niche. Whether you're in healthcare, finance, or any other highly specialized field, AI can be tailored to handle domain-specific problems. Rather than Googling "how to implement XYZ regulation in code." you can directly ask AI agents to implement the same in a prompt.
Advanced: AI Agents for Coding
AI agents are AI programs designed to perform tasks autonomously by interacting with their environment making informed decisions based on collected data to achieve specific goals. Particularly for coding-related tasks, there are coding AI agents, such as Devin and Open Hands, that aim to do anything a human developer can: modify code, run commands, browse the web, call APIs, and even copy code snippets from StackOverflow.
Ethical Considerations
The future of coding looks impressive, with AI poised to transform how we develop software. However, with great power comes great responsibility. While AI can automate much of the coding process, remaining vigilant about ethical considerations is crucial. Potential job displacement is a significant concern, as automation could reduce the demand for human coders. Additionally, bias in AI-generated code can perpetuate existing inequalities and introduce new ones if not properly managed. Ensuring transparency, fairness, and accountability in AI systems is essential to harness their full potential while mitigating risks. As we embrace these advancements, a balanced approach that prioritizes ethical standards will be critical to a sustainable and inclusive future in coding.
Conclusion
Generative AI is transforming coding, making it faster, easier, and more efficient. From code completion to debugging, refactoring, and documentation, AI tools streamline the development process and improve productivity. As you integrate generative AI into your workflow, you’ll find yourself able to focus more on creative problem-solving and less on routine coding tasks.
Start exploring the possibilities with these generative AI tools. Check our list of 14 best prompt engineering courses to find the one for yourself!