Optimizing AI for Your Daily Frontend Work

Most front-end developers I know are already using tools like GitHub Copilot and ChatGPT as part of their daily routine. Whether you're writing a React component, debugging an error, or writing that one test you've been avoiding — AI is quietly helping us get things done.

But the thing is, most of us use these tools out of the box. We take what recommendations are thrown our way and maybe tweak them to fit our project. Which works… kind of. But not always.

The real power comes when you customize these tools to match how you work. What if Copilot wrote code that already followed your team’s style guide? What if ChatGPT understood you were using Tailwind, or TanStack Query, or whatever your stack is?

That’s what this article is about — not just using AI, but optimizing it for your day-to-day frontend workflow.

Teach Copilot How You Code

GitHub Copilot is great at speeding up your day-to-day work — from generating boilerplate to suggesting logic before you’ve even finished typing. It’s like pair programming with an assistant who’s always one step ahead.

But sometimes Copilot’s suggestions can feel a bit off — maybe it gives you a class-based component when you always use hooks, or suggests plain CSS when your whole project is in Tailwind. That’s where custom instructions come in — and they’re a brand new feature (introduced in March 2025) available in GitHub Copilot for VS Code.

By adding a .github/copilot-instructions.md file to your project, you can guide Copilot to follow your coding standards. It’s like giving it a bit of onboarding — “hey Copilot, this is how we do things around here.”

Here's a simple example:

Once you’ve added it, you’ll notice the difference right away. Copilot starts giving suggestions that feel more like your code — not something copied from a random GitHub repo. It adapts to your style and project setup, which means fewer edits and more focus on building.

Why is this so helpful? Because Copilot is trained on millions of public code examples — which means it doesn’t know anything about how your team writes code unless you tell it. With custom instructions in place, you spend less time fixing or rewriting suggestions and more time actually building. Especially in frontend work, where things like prop naming, component structure, and styling approaches really matter.

In my experience (so far):

I’ve only been using custom instructions recently, so I’m still discovering all the ways it can help. But even in this short time, the impact has been noticeable:

• I rarely need to edit Copilot’s suggestions anymore — I can just accept them and move on
• It truly feels like a pair programmer that knows our codebase
• You can even prompt it to generate boilerplate (more on that later in this article)
• It’s super easy to tweak or expand the instruction file as your setup evolves
• And best of all: it’s shared across the whole team, so everyone benefits

It’s a small addition to your project that brings a huge productivity boost — especially if you're working in a team or larger codebase where consistency is key.

Better Prompts, Better Results

Just like Copilot benefits from custom instructions, AI tools like ChatGPT work best when you guide them with the right context — especially when you're dealing with more complex frontend problems.

When I want to generate an AI image, I don’t just say “make a picture of a mountain.” I first ask ChatGPT:

“Give me a good prompt to create an AI image about a mountain scene. If you need more details, ask me.”

That little step — treating AI like a creative partner — helps me refine the idea before jumping in. I get follow-up questions, better structure, and a much more focused final result.

It’s the same with code.

Instead of asking something generic like “how do I fetch data in React?”, I’ll start with:

“I’m using React with TypeScript and TanStack Query. How should I fetch and cache paginated data for a list view?”

The difference is huge. With just a bit of context, AI stops being a Stack Overflow clone and becomes something more helpful — it starts thinking along with you, like a teammate who understands your stack, your patterns, and your intent.

If you treat the prompt as a conversation and not just a question, the answers get much better.

Let AI Handle the (Boring) Boilerplate

One of my favorite use cases for AI is letting it help with the stuff that we all know is important, but don’t always have time (or energy) for: writing documentation, renaming variables, and improving readability.

You can feed your function or component into an AI tool and ask:

“Here’s a component. Can you write a JSDoc comment, suggest better variable names, and point out any improvements?”

You’ll usually get a surprisingly decent review. AI might point out a vague variable name, suggest breaking part of your logic into a helper function, or even highlight edge cases you hadn’t considered. And the best part — it does all that in seconds.

You can also have AI generate documentation or README sections from actual code. Just say:

“Here’s a utility function. Can you write a short description of what it does and how to use it?”

And if you're working with TypeScript, it can often infer types and descriptions that save you time on writing out all the details manually.

But it also works the other way around: you can describe what you want in a comment, and let AI do the rest. For example:

// Map an array of users to an array of objects with id and fullName

CoPilot will often suggest:

const simplifiedUsers = users.map(user => ({
  id: user.id,
  fullName: `${user.firstName} ${user.lastName}`
}));

That’s a small but helpful boost — you didn’t have to think about syntax, just described what you needed, and AI filled in the rest. This is great when you’re shaping data for a component, preparing API responses, or writing utility functions.

What I like is that you stay in control — AI gives you suggestions, but you decide what fits. It’s like having a junior dev who’s surprisingly good at documentation and boilerplate.

This kind of workflow gets even better when you combine it with the earlier tips: give the AI context, keep your instructions clear, and use it as a thinking partner — not a magic wand.

Conclusion: Make AI Work Like You Do

Tools like GitHub Copilot and AI assistants have become part of the modern frontend workflow — and that’s a good thing. They save time, reduce repetitive work, and help us stay focused on the more interesting parts of development.

But like any tool, they’re way more effective when you shape them to fit how you work. Whether it's adding custom instructions to Copilot, giving context-rich prompts to AI, or using it to clean up your code and write docs — small optimizations lead to big results.

You don’t need to change how you develop — just give AI a little help understanding your setup. The better it understands your project, your style, and your goals, the more valuable it becomes.

Start small: write a better prompt, add a .copilot-instructions.md, or ask AI to help clean up a function.
You’ll be surprised how quickly it starts feeling less like a tool — and more like a teammate.