Posted On:Sep 9, 2025

Best Ways To Use Artificial Intelligence In Software Development

Written By: Omid Ardestani

Abstract

AI is helping developers write code faster than ever.

But when developers use AI without understanding the output, they risk introducing hidden bugs, tech debt, and long-term maintainability issues.

This article proposes a structured, practical way to use AI tools to accelerate development while maintaining and improving your technical proficiency.

Introduction

Why Are Some Senior Developers Avoiding AI?

Despite the hype, many experienced developers remain cautious, and for good reason:

Loss of deep understanding: When you don’t write the code, you may not fully grasp how it works.
Debugging black boxes: Fixing a bug in auto-generated code is harder when you didn’t design the logic.
Technical debt: Code shipped without full comprehension is harder to maintain and scale later.
Security and ethics: Concerns over code licensing, private data leaks, and over-reliance on external tools.

The Hidden Cost of Speed

Sure, AI can speed up development—but are we trading mastery for momentum?

It’s one thing to write code quickly. It’s another to own the code, defend its design, and evolve it over time.
Without understanding what we ship, we risk building unstable foundations that will collapse under complexity.

Common Ways Developers Use AI Today

AI is already embedded in the modern workflow. Developers regularly use it to:

Write boilerplate or repetitive code
Explain legacy or unfamiliar codebases
Generate test cases
Refactor or review code

These are all valuable and time-saving. But a risky pattern is emerging:

A Risky Pattern That’s Emerging

Developers use general prompts to write full classes or functions (e.g., “Write a C++ thread pool”)
They solve algorithmic problems using AI output without reading or tracing the logic
AI-generated logic is integrated directly into production without thorough review

The result?
Code that works—but:

Few understand why it works
No one knows how to maintain or debug it
Important trade-offs and design patterns are missed or misapplied

“Using AI without understanding the output isn’t collaboration—it’s delegation without accountability.”

AI-Augmented Workflow for C++ Development

A practical methodology for developers who want to enhance productivity with AI while maintaining control, quality, and engineering standards.

Code Implementation

Use AI to refine design thinking, not to skip it.

Design the class or function manually, with initial structure and intent.
Ask AI to suggest improvements, specifically:
- Suggest an applicable design pattern (e.g., Singleton, Visitor, Builder, PIMPL).
- Recommend interface refinements or modularity improvements.
Review AI suggestions critically:
- Accept only those that match your context, performance needs, and architecture.
- Adjust manually if AI overengineers or misinterprets.
Ask AI to implement the class or functionbased on your final design.
- Include constraints (e.g. no STL, support for C++20 concepts, etc.).
Review and optimize the AI output.
- Focus on memory safety, exception guarantees, and performance.
- Write the unit tests yourself for critical understanding and validation.

Test Implementation

Use AI to assist in coverage planning and test writing — but maintain ownership of test quality.

For each class/function, ask AI to:
- Suggest comprehensive unit tests.
- List corner cases and edge conditions.
- Define test zones: normal behavior, error handling, boundary conditions.
Use AI-generated tests as a starting point, then:
- Validate correctness of assertions and mocks.
- Modify for framework style (e.g. Google Test, Catch2).
Apply mutation testing manually:
- Modify logic in key branches.
- Rerun tests to validate their sensitivity.
- Adjust test cases to improve coverage if mutations pass.

Documentation

Documentation should be clean, minimal, and readable — AI should support clarity, not replace understanding.

Use clear, expressive variable and function names to act as self-documentation.
Apply logical segmentation inside functions (using whitespace or comment blocks).
Add brief manual comments above each segment that explain why (not just what).
After writing, ask AI to:
- Generate a natural-language summary for each class/function.
- Create or refine Doxygen-style documentation if needed.
- Suggest comment improvements for clarity.

Tip: Ask AI for documentation in different styles (e.g. Markdown, Doxygen, or internal API wiki format).

Code Review

AI should be used as a second reviewer, not the only one — and should never review its own code.

Always write your implementation independently before review.
Add meaningful file headers that include:
- Purpose of the file/module
- Date, author, relevant tickets or links
Ask AI to:
- Review the code for performance, memory issues, and clean code
- List suggested improvements or risky areas, segmented by function or file.
Follow up by:
- Validating AI’s suggestions manually.
- Prioritizing fixes based on severity (e.g. UB > clarity > style).

Assistant Usage

Replace time-consuming searching and debugging with focused AI questions.

When you hit a bug or strange behavior:
- Paste the minimal reproducible code and error into the AI.
- Ask for explanation, likely causes, and fix examples.
When learning or recalling a technology:
- Ask AI to provide a clear example (e.g., “Show me how to use std::variant with visitors in C++20”).
When dealing with legacy code or build systems:
- Ask AI to explain macros, CMake configurations, or build errors.
For optimization:
- Ask AI to explain performance implications (e.g., "std::vector<bool> pitfalls" or "cache-aligned structs").
Use AI to translate concepts from other languages into idiomatic C++ (e.g., how to replicate Python decorators in C++20).

Toolchain Integration (Optional but Recommended)

Make AI tools part of your daily dev environment:

Use Case Example (in C++)

Let’s imagine you're writing a thread-safe queue in C++. Here’s how AI can help—and how you can still master the code.

Problem Statement: Design and implement a thread-safe queue in C++ that supports multiple producers and consumers.
Prompt to ChatGPT: “Can you implement a thread-safe queue in modern C++ using mutexes and condition variables?”
AI-Generated Code (Simplified)

How to Review the Output

Ask yourself:

Are all member accesses properly synchronized?
What happens if dequeue is called after destruction?
Is it exception-safe?
Are enqueue and dequeue blocking as intended?

What to Question

Should the queue support timeouts or non-blocking behavior?
Is std::queue the right container?
Should dequeue() return a std::optional<T> instead of blocking?

How to Rewrite Part of It

Replace std::queue with std::deque for thread-safe flexibility
Add a flag to support graceful shutdown
Use std::optional<T> or custom error handling for non-blocking dequeue
Wrap critical sections with RAII abstractions

What You Learn

How condition variables actually work in real-world C++
How to spot edge cases AI might not consider
How to structure concurrency primitives with clear lifecycle management

How It Helps in the Future

You’ll be able to debug deadlocks, identify race conditions, and extend the queue (e.g., with backpressure or metrics)
You’re not just copying code—you’re building confidence in multithreaded design patterns

Conclusion

AI tools are incredible accelerators. But speed is not mastery.

Use AI to move faster, experiment more, and improve design—but never skip the learning phase. The best developers will:

Be able to review and refactor AI-generated code
Know why the code works—not just that it works
Write and ship code they can defend, debug, and evolve

The best developers won’t be replaced by AI—they’ll be the ones who know how to use it without losing themselves.

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