A Different Way to Build
Ask a developer from 2020 how they build a web application, and you'll hear about setting up local environments, installing dependencies, configuring webpack, debugging npm errors. The process is well-established: you think about what you want, translate that into code syntax, and manually type it out character by character.
Ask a developer in 2025 the same question, and the conversation increasingly centers on prompts. Not because the underlying code has changed—browsers still run JavaScript, servers still serve HTML—but because the interface between human intent and working code has fundamentally shifted.
This guide is about that shift. Not the theory of it, but the practical how-to: what does it actually look like to build software by describing what you want?
The Core Difference: Imperative vs. Declarative
Traditional programming is imperative. You tell the computer exactly how to do something, step by step:
for (let i = 0; i < items.length; i++) {
if (items[i].active) {
results.push(items[i].name);
}
}Every semicolon matters. Every bracket must be balanced. You're not just describing what you want—you're specifying the exact mechanism.
AI-powered development is declarative. You describe what you want, and the AI figures out the how:
"Get the names of all active items from this list."
The barrier has moved. It's no longer about knowing syntax; it's about being able to explain your logic clearly. If you can describe what you want in plain English, you can build software. This is genuinely new.
That's not to say coding knowledge has become useless. Understanding how loops work helps you verify that the AI's implementation is correct. Understanding databases helps you ask for the right kind of data persistence. Domain knowledge still matters—but it's shifted from "I must implement this myself" to "I must understand this well enough to direct and verify the AI's work."
The Core Loop: Prompt, Review, Iterate
Every AI workflow follows the same basic pattern, whether you're using BYOB, ChatGPT, or any other AI tool. You prompt, you review the output, and you iterate until you're satisfied.
Starting with Structure
When you're building something new, start with the structure before worrying about details. You don't pick paint colors before the walls exist.
In practice, this means your first prompt should describe the overall shape of what you're building:
"Build a dashboard for a fitness tracker. It should have a sidebar navigation, a main area with activity charts, and a profile section in the corner."
This gives the AI context about the kind of thing you're building. It can make intelligent choices about layout, component structure, and styling that would otherwise require endless back-and-forth.
Refining Iteratively
Once you have a structure to react to, you can get specific. The key insight here is that AI works best when you give it clear, bounded requests rather than vague directions.
Less effective:
"Make it look better."
More effective:
"Change the charts from line charts to bar charts. Make the 'Calories Burned' card red to give it visual urgency. Increase the font size on the sidebar labels to 16px."
The second prompt tells the AI exactly what to do. There's no ambiguity, no guessing about what "better" means. You get what you asked for, and if it's not quite right, you can give equally specific feedback.
Adding Logic
This is where AI development really shines. The logic that would normally require you to research API patterns, figure out state management, and debug race conditions—all of that gets handled by describing the behavior you want:
"When I click the 'Log Workout' button, open a modal form that asks for exercise type and duration. When I submit the form, add the workout to the list and update the total minutes on the dashboard."
The AI generates the form, handles the modal state, updates the data, and triggers the UI refresh. You described the user experience; it handled the implementation.
How the AI Understands Context
A reasonable question: how does the AI know what you're talking about when you say "make the header bigger"? How does it know which header, in which file, with what current styling?
The answer is the context window. When you send a prompt to BYOB, the AI doesn't just see your message. It sees:
Your current files, your project's styling conventions, your conversation history—all of this gets included as context. When you say "the header," the AI looks at your project's files and finds the component that matches. When you say "bigger," it looks at the current font-size and picks a reasonable larger value.
This is why AI development feels more natural than you might expect. You don't have to spell out every detail because the AI can infer from context. It's more like giving instructions to a colleague who's been working on the project with you than like writing documentation for someone who's never seen the code.
Writing Better Prompts: The C.I.A. Framework
Good prompts tend to include three elements. We call it the C.I.A. framework:
Context: Who are you, and what's the situation? This helps the AI understand what kind of output is appropriate."Act as a senior frontend engineer building for mobile users."Instruction: What do you actually want done? This is the core of your request.
"Create a sticky header that collapses as the user scrolls down."Artifact: What format should the output take? This prevents ambiguity about what you expect.
"Use CSS animations for the collapse effect, and make sure it works on Safari."
You don't always need all three, but when you're getting unexpected results, adding more context almost always helps. The AI is trying to satisfy your request, but it can only work with the information you give it.
Learning by Doing
The best way to get comfortable with AI development is to build something. Not to read about it, not to watch tutorials, but to open a project and start prompting.
Start simple: a personal portfolio, a landing page for a side project, a tool that solves a small problem you actually have. Describe what you want, see what comes out, and iterate until it's right.
The prompts will feel awkward at first. You'll be too vague, then too specific, then you'll find the balance. You'll learn what the AI handles well and where it needs more guidance. You'll develop an intuition for how to break complex requests into manageable steps.
That intuition is the real skill. The AI handles the syntax; you develop the ability to direct it effectively. One takes years of practice to learn. The other takes a few hours of experimentation.
Start building