How Programmers Use AI To Code Smarter and Faster

Key takeaways

• AI tools can slow you down if you aren't careful. In one study, developers predicted that AI would boost productivity, but it actually took them 19% longer to complete tasks.
• AI is reshaping our core workflows in real time. AI's capabilities are evolving to help with software development, from boilerplate generation to debugging.
• Both clients and developers need to understand AI's role. AI has its limitations, and understanding what it can't do is just as vital as knowing what it can do.

Like many other people working in tech, I've found that while AI tools promise speed, they don't always deliver — unless you use them thoughtfully. The wrong AI application can turn a simple coding task into an overly complicated jumble, which is the exact opposite of what we need.

This is a common hangup. So, let's master the how. I'll explain how to use AI in coding based on real use cases I've experienced. We'll also look at how artificial intelligence is changing expectations in software engineering, especially regarding code quality and how beginners learn the fundamentals. 

How to use AI in coding: real use cases for today's programmers

In a recent study from Model Evaluation and Threat Research (METR), developers predicted that AI would cut their task completion time by around 24%. Instead, they actually took 19% longer. That's a fascinating stat that really sticks with me and drives home the point: How you use AI matters more than whether you use it at all.

I've used AI coding tools for both personal and client-facing projects, and there are some notable advantages. Mainly, they've entirely changed how I address repetitive tasks like debugging and even planning. How does this fit into a real-world coding workflow? Let's review some specific stages of this life cycle, with examples and notes from my personal experiences.

Writing and completing code with AI

Staring at a blank file is daunting. But, if I rely on AI to help scaffold the structure of my code, I can officially get "started" so much faster. This streamlines my whole coding routine. AI can fill in the boilerplate and suggest syntax in real time. It's sort of like pair programming, except the machine never gets tired.

AI-generated code suggestions now happen inside the integrated development environment (IDE). These are powerful, advanced large language models (LLMs) from companies like OpenAI and Anthropic. These models interpret your input, anticipate your intent, and then write code that's surprisingly accurate.

For example, I asked AI to create a simple homepage for a car rental service. Using the prompt "Generate a simple homepage using HTML and CSS" directly in my IDE, I got the following results, which I could customize to fit my design and project goals. 

Some of the top tools I've used for writing code with AI include:

• GitHub Copilot. Works directly in VS Code and other IDEs to autocomplete code in real time based on context and prompts, using OpenAI's Codex model.
• Claude. A strong alternative for more structured completions, Claude is great for template-heavy frameworks or when you want code with added explanations.
• GPT-5. Ideal for coding from prompts, breaking down logic problems, or creating AI-generated code snippets for new projects. GPT-5 is accessible directly from ChatGPT or via OpenAI’s APIs. 

Reviewing, optimizing, and refactoring code

I've long believed that getting a second set of eyes on your code is a necessary step. AI has made it easy and instant. Instead of waiting for a peer review, I run my code through AI to catch bugs and flag vulnerabilities. I also use it to clean up my functions before I commit.

AI-powered code review tools analyze your code's structure, logic, and syntax and offer improvements for style and security. This is great for beginners learning to structure better functions and experienced software engineers refining a complex codebase. These tools can make a real difference.

For example, in my car rental website project, I wrote the following JavaScript function that calculates the total rental cost, and if the user rents a car for more than seven days, it applies a discount. While the code works, it has one major bug; it only charges the customer for one day, meaning the business would lose revenue for the other six days. The function looked like this:

function calculateTotalCost(days, rate) 
{if (days >= 7) 
{const weeks = Math.floor(days / 7);
const remainingDays = days % 7;
const weeklyCost = weeks * rate * 0.9; // 10% discount for full weeksconst dailyCost = remainingDays * rate;
return weeklyCost + dailyCost;
} else {
// No discount for rentals less than 7 days
return days * rate;}}

I used ChatGPT to review the code, and thankfully, it identified the error and offered valuable insight:

Some standout tools here include:

• Codacy. Automates code quality checks and flags potential issues before they become real problems.
• DeepCode AI. Uses AI agents trained on millions of codebases to surface issues and optimize performance.
• Sourcery. Focused on Python, this tool helps with real-time refactoring and optimization inside your IDE.

Debugging and troubleshooting with AI tools

Finally, we get to debugging. Debugging is my absolute least favorite part of my coding workflow. But if I have a little help from AI, it's infinitely less frustrating. In fact, it becomes more about discovery. Using AI tools, I can describe a bug in natural language and get back a list of potential fixes or code templates. It can even give me step-by-step troubleshooting tutorials.

To illustrate, I faced the dreaded CORS error while connecting my front end to the back end. Whatever technique I tried, it always resulted in this error:

Access to fetch at 'url' from origin 'url' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource

I used ChatGPT to help me debug the error using the following prompt.

I am experiencing a cors error while trying to connect my frontend to my NODEJS backend. Could you help me resolve it. The error is "Access to fetch at 'url' from origin 'url' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource"

I got this result, which amazingly helped resolve the issue, leading to a seamless integration between the front end and back end.

Some of the best AI coding assistants for debugging and troubleshooting include:

• ChatGPT. Great for describing errors in natural language and getting easy-to-read explanations or alternative code snippets.‍
• Amazon Q Developer. Formerly CodeWhisperer, it offers real-time bug fixes, context-aware suggestions, and deeper AWS integration for troubleshooting.‍
• Tabnine. Offers context-aware debugging suggestions and templates that you can customize for faster fixes.

Top AI coding tools (and how to use them)

Now that I've told you what AI can be used for, let's look at some specific AI tools. I've tested some while coding, and a few stand out for everyday use. Some of them are great for writing clean, high-quality functions. Others shine when you need API help or want to learn a new framework from scratch.

1. GitHub Copilot

GitHub Copilot integrates directly with your favorite IDE (like VS Code). Essentially, it acts like a smart autocomplete engine for code. It can streamline workflows by filling in lines of code, explaining syntax, and suggesting full functions based on your coding context.

• Pricing. Free forever plan available with limited functionality. Pro plan starts at $10 per month (or $100 per year) for individuals, with business plans available.
• Features. Autocompletes code, explains syntax, adapts to your style over time.
• Use cases. Great for writing boilerplate, exploring new libraries, or navigating open source code faster.

2. Claude by Anthropic

I like Claude for its strengths in natural language processing. It's perfect when I need help writing or improving documentation, or simply translating logic into plain English. It's also useful for debugging in collaboration with a conversational AI agent.

• Pricing. Free plan available with limited features; Pro version is $17 per month (billed annually) and includes Claude Code.
• Features. Summarizes code functions, generates helpful comments, and reviews logic with clear explanations.
• Use cases. Best for documentation-heavy projects, onboarding flows, or helping beginners understand complicated tutorials and docs.

3. Gemini from Google

Need to build interactive components quickly? Try Google's Gemini. The platform is impressive in its support for front-end prototyping and multilanguage development. I turn to it when I need to test APIs or integrate across Google services.

• Pricing. Currently available via Google Workspace and AI Studio, with some features free. Advanced subscription features available through Google AI Pro ($19.99 per month) or Google AI Ultra ($249.99 per month).
• Features. Supports multiple programming languages, prototyping frameworks, and fast iterations.
• Use cases. Good pick for front-end development, API testing, and rapid prototyping with AI-enhanced design tools.

What AI really does (and doesn't do) in software development 

As I've mentioned, AI coding tools are powerful, but they're not magic. The best approach is to think of them as your assistants. They can help with speed, structure, and suggestions, but they’re not substitutes for necessary critical thinking. They also aren't a replacement for deep software development expertise.

Let's further break down what today's AI-powered tools can do vs what they can't.

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FAQ about how to use AI in coding

If you still have questions about how AI fits into modern programming, I can help. Let's break down the questions I hear most often.

Which AI coding tools deliver real value for developers?

Real value is a matter of opinion; it'll come from finding functionality that fits your goals and workflow, whether working in VS Code, supporting specific frameworks, or integrating with your current API stack.

These are the tools that I think are worth your while:

• GitHub Copilot. Delivers reliable, context-aware suggestions and full code blocks across major IDEs.
• Claude. Especially helpful for summarizing functions, writing docs, and debugging using natural language prompts.
• Gemini. Flexible tool for developers working across multiple programming languages, APIs, and prototyping projects.

When you're comparing AI coding tools, pay close attention to a few things: their integration with your preferred IDE, support for your tech stack, and cost model. The right tool will satisfy your needs on all three fronts.

What programming languages can AI work with?

Most AI tools support popular programming languages. Of course, this support will vary slightly depending on the tool and the LLMs powering it. In general, though, developers can expect strong performance in the following:

• Python. Especially well-supported due to its simplicity and widespread use in AI, scripting, and back-end dev.
• JavaScript. Commonly supported for both front-end and full-stack development tasks.
• Java. Ideal for enterprise-scale projects, often well-supported in AI-assisted environments.
• C++. While not always the primary focus, some AI tools support this language for performance-critical applications.

Can AI replace coders?

This is the most common question I hear, and the short answer is no. While artificial intelligence can handle a lot, it's mostly relegated to repetitive tasks, which makes a coder’s day tedious. It cannot replace the nuanced problem-solving and creativity of a skilled coder.

AI-powered tools have made huge strides and major advancements, but they still rely on a human being to define goals and evaluate code quality. Human input is also necessary to make sure the software actually solves the right problem. In software engineering, outcomes often depend on the coder's insight, which is something a machine can't provide.

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Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

Prices are current at the time of writing and may change over time based on each service's offerings.