This article explores how generative AI can be integrated into development. It addresses the question of maintaining quality control while generating code with AI. Aimed at developers and tech leads, it examines AI code generation tools' capabilities, risks, and opportune uses.

Can I Generate Code Using Generative AI Models? Pros and Cons

Do you feel like your dev process keeps pulling you in ten different directions? Between tight deadlines and repeating the same chunks of code, losing focus is easy.

So here’s the real question: Can I generate code using generative AI models while maintaining full quality control?

This blog is for developers, tech leads, and dev teams considering adding AI to their workflow. We will examine what these tools can do, their associated risks, and when they’re worth using.

If you’re weighing the pros and cons of AI code generation, you’ll want to read on.

What is AI Code Generation?

AI code generation is the process where generative AI models create, suggest, or complete code based on inputs like natural language prompts, comments, or partial code. These models—often large language models like GPT, Codex, or Gemini—are trained on billions of lines of publicly available code from platforms like GitHub, Stack Overflow, and open-source documentation.

These systems support multiple programming languages, from Python and JavaScript to Go and Rust, and can handle routine coding tasks such as writing functions, completing code, or suggesting logic based on existing source code.

How Does It Work?

The core technology powering generative AI code combines machine learning , deep learning models, and natural language processing.

The process typically involves:

• Parsing user input (plain language or partial code)

• Mapping it to probable code patterns

• Suggesting code snippets, completions, or entire logic blocks

Some AI code generation tools, such as GitHub Copilot , Amazon CodeWhisperer, and Tabnine, go further by personalizing suggestions by learning from the user's existing code.

Pros of Using Generative AI for Code Generation

1. Faster Coding for Repetitive Tasks

AI helps you write boilerplate code or handle repetitive tasks quickly. You can generate code in seconds instead of manually typing class definitions or API handlers.

Example: Typing create a Flask app with one GET endpoint might yield a ready-to-run code block using natural language input.

2. Multi-language Support

Many AI code generation tools can translate code between languages or generate code in various programming languages. This helps development teams manage legacy code and upgrade or refactor.

3. AI-Powered Learning

With tools like GitHub Copilot, developers can learn coding patterns by observing AI-generated code. This can also help newcomers understand how to write code efficiently in real time.

4. Code Completion and Suggestions

AI can provide real-time code completion and code suggestions tailored to your existing source code and code editors like Visual Studio Code.

5. Better Prototyping

Generative AI code can generate functional code for proof-of-concepts or MVPs. It’s especially helpful during brainstorming or hackathons when time is limited.

Cons of Using Generative AI for Code Generation

1. Code Quality and Reliability

Not all AI-generated code is optimal or secure. AI models might hallucinate libraries, suggest deprecated APIs, or produce coding errors that go unnoticed without manual review.

2. Security Risks

Using publicly available code for training raises concerns. Your AI code assistant might leak unsafe patterns or even copyrighted logic into your app.

Example: A model trained on insecure code may unknowingly repeat the same in AI code generation tools.

3. Poor Context Handling

While generative AI tools are great at autocomplete, they often miss the bigger picture, especially when generating code based on multi-file projects or architecture decisions.

4. Over-Reliance Reduces Understanding

Constantly relying on AI-powered coding assistants might make developers lazy about understanding core logic. This is especially risky in teams that value strong team coding practices.

5. Lack of Explainability

Although some tools can explain code, many don’t explain why a particular solution was suggested. That can make debugging harder when errors arise in AI-generated logic.

Key Use Cases of Code Generation Tools

Practical Example

A WordPress developer wants to create a REST API endpoint using PHP. Instead of Googling or copying from Stack Overflow, they input:

“Create a REST API in PHP to return a list of blog posts”

The AI tool generates:

Copy
1add_action('rest_api_init', function () {
2  register_rest_route('myplugin/v1', '/posts', array(
3    'methods' => 'GET',
4    'callback' => 'get_blog_posts',
5  ));
6});
7
8function get_blog_posts() {
9  $posts = get_posts();
10  return $posts;
11}

This is functional, but still needs security checks—AI-generated code is not always production-ready.

Want to build an app fast without writing code? Just describe what you need in plain English or drop a Figma link—Rocket handles the rest. From idea to launch, create web or mobile apps in minutes at rocket.new.

When to Use Generative AI Code (and When Not To)

Final Thoughts

Can I generate code effectively using generative AI models? Yes, but with caution. Generative AI can help software developers write code, offer code completion suggestions, and speed up development, especially for manual coding and routine coding tasks.

Still, understanding the development process, maintaining code quality, and reviewing AI-generated code manually remain non-negotiable. AI coding tools are code generation assistants, not replacements.

As generative artificial intelligence evolves, so will its code generation abilities. Keep experimenting—but keep learning, too.