Using AI to Turbocharge User Authentication

A GPT-Assisted Approach

Introduction

There are several potential benefits to using artificial intelligence (AI) models like GPT-4 to generate code:

1. Increased productivity
   AI code generation can speed up the development process by writing boilerplate code, completing repetitive tasks, or even creating complex code segments. This allows developers to focus more on problem-solving and innovative aspects of software development.
2. Fewer errors
   AI models can reduce the number of coding errors by providing syntax checks and following established coding standards. They can also help debug existing code by identifying potential issues.
3. Learning and upskilling For novice developers, AI can serve as an educational tool by providing code examples and explanations. It can also help experienced developers learn new programming languages by generating code snippets in the desired language.
4. Code optimization
   AI can suggest more efficient algorithms or ways of structuring code to improve performance and reduce resource consumption.
5. Round-the-clock coding
   AI doesn't need to rest, so it can code around the clock. This can be especially useful in deadline-driven environments.
6. Personalized code generation
   Over time, AI can learn individual coding styles and preferences, tailoring its output to each developer's unique approach.
7. Assistance with large codebases
   AI can help navigate and understand large, complex codebases, making it easier to maintain and extend existing software.
8. Automated testing
   AI can generate test cases and scripts, helping to ensure code quality and stability.
9. Language translation
   AI can help in the process of migrating a codebase from one programming language to another, easing the transition.
10. Accessibility
    By reducing the need for specialized knowledge, AI can make coding more accessible to people who aren't professional developers but need to write or understand code for their work.

These benefits can have a considerable impact on the software development process, potentially transforming how code is written and maintained. However, it's also important to note that these systems won't replace human developers anytime soon; instead, they will serve as powerful tools that can augment and enhance human capabilities.

GPT Prompt

I would like a code library in both Typescript and Python that makes it easy to work with 
and help me with authenticating a user given a username (in the form of an email address) 
and a password. I'd like the following functions/methods:

1. Determine that an email address is valid

2. Determine that a password is valid. The password must be at least 12 characters long, 
   have mixed upper and lower case letters, numbers and at least 1 special character

3. Split a valid email address into the username, the domain name and the top level 
   domain (for example, [email protected] would be split into the three strings "hello" 
   as the username, "email.com" as the domain and "com" as the top level domain.

4. Hash a password using the bcrypt library or similar

5. Validate a password against it's hash

Prompt Overview

The prompt for GPT in this case was an overall problem statement followed by specific requirements regarding the functions/methods needed. In this case, that included the conditions for valid email and password, and requirements for hashing and validating a hashed password. Note there was no strict need to provide names for the functions/methods as GPT created appropriate names however if you wanted to assign specific names you could include them in the prompt.

GPT Response

We will create a simple library for user authentication. This library will include functions to validate an email address and password, split an email address into its components, hash a password, and validate a password against its hash.

Generated Typescript:

import * as bcrypt from 'bcrypt';

class UserAuth {
  static validateEmail(email: string): boolean {
    let emailRegex = /^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+(\.[a-zA-Z0-9-]+)*\.[a-zA-Z]{2,}$/;
    return emailRegex.test(email);
  }
  
  static validatePassword(password: string): boolean {
    let passwordRegex = /^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[@$!%*?&])[A-Za-z\d@$!%*?&]{12,}$/;
    return passwordRegex.test(password);
  }

  static splitEmail(email: string): [string, string, string] {
    const [username, domain] = email.split('@');
    const domainParts = domain.split('.');
    const topLevelDomain = domainParts[domainParts.length - 1];

    return [username, domain, topLevelDomain];
  }
  
  static async hashPassword(password: string): Promise<string> {
    const salt = await bcrypt.genSalt(10);
    const hashedPassword = await bcrypt.hash(password, salt);
    return hashedPassword;
  }
  
  static async validateHash(password: string, hash: string): Promise<boolean> {
    return await bcrypt.compare(password, hash);
  }
}

Generated Python:

import re
from typing import Tuple
from passlib.hash import bcrypt

class UserAuth:
  @staticmethod
  def validate_email(email: str) -> bool:
    email_regex = '^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+(\.[a-zA-Z0-9-]+)*\.[a-zA-Z]{2,}$'
    return bool(re.match(email_regex, email))
  
  @staticmethod
  def validate_password(password: str) -> bool:
    password_regex = '^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[@$!%*?&])[A-Za-z\d@$!%*?&]{12,}$'
    return bool(re.match(password_regex, password))
  
  @staticmethod
  def split_email(email: str) -> Tuple[str, str, str]:
    username, rest = email.split("@")
    domain_name, top_level_domain = rest.split(".")
    return username, rest, top_level_domain
  
  @staticmethod
  def hash_password(password: str) -> str:
    return bcrypt.using(rounds=12).hash(password)
  
  @staticmethod
  def validate_hash(password: str, hashed_password: str) -> bool:
    return bcrypt.verify(password, hashed_password)

GPT Response Overview

1. In the example above, we've set up a simple structure for our library.
2. Each method corresponds to one of the tasks we need to accomplish.
3. We're using the bcrypt library for hashing passwords due to its security and ease of use.

Benefits and Conclusion

Using GPT for this task not only reduces the coding burden but also decreases the likelihood of errors that can arise in manual coding, especially in the repetitive aspects of the process. Moreover, it facilitates efficient prototyping and allows developers to focus more on the unique, complex aspects of their projects.

Tasks like user authentication is a common but crucial element of software development. While AI can't replace developers, tools like GPT can significantly aid in the development process. By automating repetitive coding tasks, AI allows developers to focus on the more challenging and creative aspects of software development.

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