Prompt. Build. Ship.:: AI for Web Devs Build x3 Times Faster - страница 3

In my free time, I enjoy mentoring and teaching—especially around AI for coding, web development, and web design.

If you’d like to check out my latest work, head over to my website at ilyasseisov.com. You can also connect with me on LinkedIn.

Let’s quickly go over the tools and tech stack you’ll need for this course.

First, you’ll need a code editor. You can use anyone you like, but I personally recommend VS Code—it’s the most popular choice among developers and works great for what we’ll be doing.

Second, let’s talk about the tech stack. You should have a basic understanding of HTML, CSS, and JavaScript. That said, it’s not strictly required to know these specific technologies. Since we’ll mostly be focusing on the fundamentals of using AI in coding, you’ll still be able to follow along even if your background is in another language.

Hello, and welcome to Module Two!

In this module, we’ll explore the following topics:

We’ll start with the basics of general AI and how it actually works.

Then, we’ll dive into some of the most popular general-purpose AI tools, and we’ll break each one down in detail.

Next, we’ll look at the four main ways to interact with AI.

And finally, we’ll wrap up with an introduction to prompt architecture—how to structure your prompts effectively.

Alright, let’s jump in and get started!

Hello and welcome to Lesson One!

In this lesson, we’re going to talk about how generative AI actually works—specifically, text-based generative AI. Why text-based? Because code is essentially just text.

Let me show you a couple of slides from Google’s official explanation. According to Google, generative language models learn patterns in language by being trained on huge amounts of text data—think terabytes of it. Engineers feed this massive data into the system, and the AI learns from it.

Here’s the key point: the responses you get from a generative AI model aren’t calculated using fixed formulas—they’re predicted based on patterns the model has seen during training.

Let me say that again: it’s prediction, not exact calculation.

For example, take this sentence:

“I’m making a sandwich with peanut butter and…”