Using AI to Code 10x Faster – The Techniques I’ve Learned

As a developer constantly seeking ways to optimize my workflow, I’ve found one of the greatest breakthroughs in recent years has been using artificial intelligence to accelerate my development process. When I first heard of tools like GitHub Copilot and ChatGPT for coding, I was skeptical. But after a few months, I realized these AI tools aren’t just helpful — they’ve fundamentally changed the way I write code.

In this article, I’ll share the key techniques I’ve learned that have helped me code up to 10x faster using AI. Whether you’re a seasoned software engineer or a budding programmer, these tips could dramatically improve your productivity.

1. Leverage AI for Boilerplate Code

Let’s face it — a lot of programming involves writing repetitive, boilerplate code. Whether it’s setting up a new React component, initializing a REST API, or writing basic CRUD operations, these tasks are time-consuming and often error-prone.

AI tools let me generate these chunks of boilerplate code in seconds. For instance, when working on a Django project, I can ask the AI to create a model with specific fields, and it instantly generates all the structure I’d typically take several minutes to write manually. Over the course of a week, the saved time becomes substantial.

2. Use Natural Language to Explain Code

Sometimes the fastest way to understand or debug a code snippet is to ask the AI to explain it to you. Instead of searching Stack Overflow or trying to trace every line yourself, you can simply paste in a function and prompt the AI with something like: “Explain what this function does.”

This especially helps when working with unfamiliar or legacy codebases. Not only does it clarify complex logic, but it also serves as a great learning tool. I’ve even used it to generate in-line comments automatically, making my code more readable and maintainable.

3. Speed Up Debugging

We’ve all encountered those cryptic error messages that derail your coding flow. Nowadays, instead of Googling the stack trace, I paste the error and context into my preferred AI tool. The feedback is almost instant and typically spot-on. It often suggests the root of the problem, why it’s happening, and how to fix it.

The AI doesn’t just guess — it analyzes the code around the error and walks you through the logic. This is like having a senior developer pair-program with you 24/7.

4. Generate Test Cases Instantly

Writing tests used to be something I avoided because it felt tedious. But AI has changed that. Give it a function and prompt it with: “Write unit tests for this function.” Within seconds, you get well-formatted and effective test code that covers different edge cases.

Even better, it can generate tests in the specific testing framework you’re using, whether that’s JUnit, pytest, or Mocha. This alone has improved both the speed and quality of my development process.

5. Build Mini Tools and Scripts In Minutes

Ever wanted a quick command-line utility to bulk rename files or parse a CSV file in a specific way? Before, I’d either search GitHub or write it from scratch. Now, I just describe what I want in plain English, and AI writes the script.

Here’s an example request I made recently: “Create a Python script that renames all images in a folder to a timestamp-based filename.” Within seconds, it generated the perfect solution. I fine-tuned a few lines, and I was good to go.

6. Accelerate Learning New Frameworks and Languages

AI isn’t just great for speeding up experienced developers; it’s an incredible learning companion too. When I started exploring Rust, I used AI to write simple programs, explain syntax, and offer idiomatic solutions. It cuts down the learning curve drastically.

Tip: Always double-check code generated in unfamiliar languages. AI is powerful, but it’s not always perfect — do your due diligence.

7. Create Smarter Documentation

Documentation isn’t just for others; it’s for future-you. Unfortunately, it often gets neglected. AI tools can generate comprehensive documentation for classes, APIs, and functions without you needing to write everything by hand. A quick prompt can produce:

• Function signatures with parameter explanations
• Usage examples
• Expected input and output

You get more complete documentation with less effort, which dramatically improves code readability and onboarding speed for teams.

Final Thoughts

AI has become my coding co-pilot — not taking my place, but empowering me to work faster and more effectively. It’s like having a partner that never sleeps and is always ready to help. The key is to integrate AI into your development workflow intentionally and thoughtfully.

The future of programming is not just about being a better coder. It’s about being a smarter one — and leveraging AI might just be your biggest advantage.