Does Writing Clean Code Still Matter in the Age of AI Coding?

Benny March 6, 2025 0 comments

For several years, a copy of Code Complete by Steve McConnell (free PDF) has been sitting on my desk. It’s one of those books that every developer intends to read (or re-read), yet in an age where AI tools like Gemini Code Assist GitHub Copilot and ChatGPT generate code in seconds, I found myself wondering: does writing clean code still matter? If AI can generate working code efficiently, is it still worth investing time in naming conventions, code structure, and maintainability?

This blog is my attempt to answer that question—both for myself and for fellow developers navigating this AI-driven era. Let’s explore why clean code remains crucial and how AI-assisted coding should be approached with a mindset that balances automation with craftsmanship.

The Core Principles of Clean Code in the Age of AI

Steve McConnell’s Code Complete outlines several key principles for writing high-quality software. Even with AI assistance, these principles remain critical for ensuring code maintainability and long-term usability. Let’s examine how each applies to AI-generated code:

1. Good Code Structure

AI can generate code that works, but often lacks logical structure and modular organization. Developers must:

• Ensure proper file and function separation to maintain readability.
• Avoid monolithic AI-generated functions—break them down into smaller, reusable components.
• Review and refactor AI-generated code to align with project architecture and design patterns.

Example: AI may generate a single function handling multiple responsibilities. Developers should refactor it using the Single Responsibility Principle (SRP) to improve maintainability.

2. Meaningful Naming

AI often suggests generic variable and function names that lack context, making the code harder to understand. To maintain clean code:

• Rename vague AI-suggested names to be more descriptive and self-explanatory.
• Follow a consistent naming convention for functions, classes, and variables.
• Ensure that names convey the purpose of the variable or function clearly, without requiring additional comments.

Example: AI might generate temp1, temp2 as variable names. Changing them to userAge or orderTotal makes the code more readable.

3. Code Readability

While AI can produce syntactically correct code, readability is not always its priority. Developers must:

• Format AI-generated code using proper indentation, spacing, and line breaks.
• Ensure that logic flows naturally, making it easy for others (or future you) to understand.
• Use consistent coding styles across the project, especially when multiple developers are involved.

Example: AI-generated one-liner functions may sacrifice readability. Expanding them into well-structured code blocks improves maintainability.

4. Minimizing Complexity

AI can sometimes generate overly complex or redundant solutions. Developers should:

• Simplify AI-generated logic by removing unnecessary steps or redundant conditions.
• Prefer straightforward solutions rather than convoluted AI-generated ones.
• Avoid deeply nested conditions and loops, which can reduce code maintainability.

Example: AI-generated SQL queries might include unnecessary joins or conditions, which should be optimized for performance.

5. Refactoring and Improving Code Quality

AI-generated code often works but isn’t optimized. To maintain code quality:

• Treat AI-generated code as a draft, not a final version.
• Continuously refactor and improve structure, making it more efficient and readable.
• Regularly review AI-generated code for potential security vulnerabilities or performance issues.

Example: AI might generate redundant database queries instead of caching results. A developer should refactor to improve efficiency.

6. Effective Comments

While clean code should be self-explanatory, some AI-generated logic may require clarification. Developers should:

• Avoid over-commenting—instead, ensure that the code itself is clear.
• Use comments only when explaining complex logic or AI-generated solutions that might be non-obvious.
• Regularly update comments to stay aligned with code changes.

Case Studies: AI Coding Failures Due to Poor Code Quality

• Tesla’s AI Autopilot Bug: A reported issue with Tesla’s AI-driven autopilot was linked to unclear and overly complex decision-making logic in the codebase, reinforcing the importance of clean, structured code.
• AI-Generated Code Vulnerabilities: A study by NYU researchers found that AI-generated code often contained security flaws, emphasizing the need for careful code review and refactoring.

How Code Review Processes Must Evolve for AI-Generated Code

• Automated Linting and Static Analysis: Tools like ESLint and SonarQube can help identify AI-generated inconsistencies.
• AI-Aware Code Review Practices: Developers should treat AI-generated code as suggestions, rather than blindly accepting them.
• Peer Review for AI-Generated Code: Teams should establish guidelines to verify AI-written code with human oversight.

How to Prompt AI Tools for Cleaner Code

To get better AI-generated code, developers should:

• Use clear, detailed prompts specifying naming conventions, coding standards, and architecture preferences.
• Request explanations so that AI-generated code includes reasoning behind design choices.
• Iterate on AI suggestions instead of taking the first response as final.

Example Prompt: Instead of asking “Generate a login function,” use “Generate a secure login function in Python using JWT authentication, following PEP8 standards.”

The Right Mindset for Developers in the AI Era

AI is a powerful tool, but it should not replace critical thinking and software craftsmanship. Developers must:

• Act as code reviewers and curators, refining AI-generated outputs rather than accepting them blindly.
• Focus on long-term maintainability rather than just immediate functionality.
• Continue following proven software development principles to ensure code quality remains high.

Final Words

The era of writing clean code is far from over. While AI coding assistants can accelerate development, they do not replace the need for human oversight, thoughtful structuring, and clean coding principles. Code Complete remains a valuable guide in this AI-driven landscape, reminding developers that well-written, maintainable code is the foundation of reliable software.

It’s neither practical nor beneficial to attempt to block developers from using AI-assisted coding tools. These technologies are here to stay, and their potential to enhance productivity is undeniable. However, the key lies in wise and responsible usage. We must encourage developers to view AI as a powerful ally, not a replacement for their expertise.

Developers who adapt their mindset—leveraging AI while still enforcing clean coding standards—will create software that is not only functional but also sustainable for the future. By embracing AI as a tool for augmentation and maintaining a steadfast commitment to code quality, we can navigate the evolving landscape of software development and build robust, reliable systems that stand the test of time.