Neural networks are the core technology behind modern AI. Inspired by the human brain, they are made up of layers of connected nodes—or artificial neurons—that pass information forward and adjust based on what they learn. Each connection has a "weight" that changes as the system trains, helping the network recognize patterns, make predictions, or generate new outputs.

What makes neural networks powerful is their ability to learn from data—not by following a fixed program, but by adjusting their inner structure based on feedback. This process is called training. During training, the network compares its predictions with the correct answers, calculates the error, and updates its weights to do better next time. After many cycles, the network becomes skilled at recognizing patterns such as the shapes of letters, the tone of a voice, or the structure of a sentence.

You can think of a neural network like a team of musicians in an orchestra. Each “neuron” is one instrument. At first, they are out of sync, but through practice (training), they learn how to adjust to each other until the whole orchestra produces a harmonious performance that fits the piece of music (the task).

The most influential neural network architecture today is the Transformer. Transformers were introduced in 2017 and revolutionized how machines understand sequences of data like language, audio, or even images. Unlike earlier models that processed input one step at a time, transformers take in the whole input at once and decide which parts are most important. This is called self-attention.

Why Transformers Matter:

• They can handle long passages of text and keep track of meaning over time.
• They are fast and efficient to train, using parallel processing instead of sequential steps.
• They work across different kinds of data—text, images, audio—enabling true multimodal AI.
• They are the foundation for models like GPT (for language), DALL·E (for images), and Whisper (for audio).

Transformers allow AI systems to do more than just analyze—they can generate and communicate. They read prompts, understand instructions, and even compose stories or build software. By learning patterns in human expression, they’ve become powerful collaborators in creative and intellectual work.

Example of a Transformer in Action: Imagine you type the sentence: “The cat sat on the mat.” A transformer doesn’t just read it left to right. Instead, it looks at all the words at once and calculates how strongly each word relates to the others. For example, “cat” is strongly connected to “sat,” and “mat” is connected to “on.” This web of connections allows the model to understand not only the meaning of each word but also the relationships that give the sentence its sense. That’s why transformers can keep track of meaning across whole paragraphs or even pages.

Large Language Models (LLMs) are AI systems trained on vast amounts of text from books, articles, websites, and other sources. They use this knowledge to understand and generate human-like language. LLMs can help with tasks like translating languages, summarizing text, answering questions, and creating content.

When we interact with an LLM, the basic unit of information is a token. A token is not exactly a word—it can be a whole word, part of a word, or even punctuation. For example, “unbelievable” might be split into the tokens [un], [believe], [able]. The model reads text as a sequence of tokens, not letters or whole words. Understanding tokens is crucial because the model predicts the next token in a sequence, step by step, to build sentences, paragraphs, or even entire essays.

An LLM is like an autocomplete system on steroids. When you start typing in your phone, it guesses the next word. An LLM works similarly, but instead of just finishing “Happy…” with “birthday,” it can generate entire paragraphs, poems, or computer programs by making the most likely prediction at each step.

Generative Pre-trained Transformer (GPT) models are a specific kind of LLM that use the transformer architecture. The term “pre-trained” means the model first learns general language patterns from huge datasets, and then it can be fine-tuned for specialized tasks like coding, tutoring, or creative writing. GPTs generate fluent, context-aware text by calculating which token is most likely to come next based on the tokens that came before.

Recent versions like GPT-4 can process and generate not just text, but also images, audio, and even video. This ability is called multimodal AI. It means one model can read, write, and "see" or "hear" at the same time—useful for creative projects, accessibility tools, or educational simulations.

How do language models guide image generators? Think of text as the brain and images as the hands. When you type a prompt like “a fox jumping over a fence in moonlight,” the language model interprets the sentence and translates it into a detailed internal structure—something the image model can understand. It’s language that organizes and controls the creation of visual, auditory, and interactive content.

Example of an LLM in Action: Suppose you ask an LLM: “Write a short story about a dragon who learns to cook.” The model breaks your sentence into tokens and looks at all the relationships between them (“dragon” relates to “fantasy,” “cook” relates to “food” and “kitchen”). Then, step by step, it predicts the next most likely token: [Once], [upon], [a], [time]… and so on, each choice influenced by the full context of the prompt. In this way, the model builds a coherent response that matches your request.

For now, our focus is on language. But keep in mind: the same underlying ideas about tokens, patterns, and prediction extend to other forms of media. In later chapters, we’ll explore GANs and diffusion models in detail to see how machines learn to generate images, sound, and beyond.

To get the most out of large language models and other generative AI tools, it’s important to learn how to write effective prompts. A prompt is the instruction or input you give to the AI. The clearer and more thoughtfully crafted the prompt, the better the result.

In generative AI systems, language is more than just input—it’s the interface. Whether you're generating text, images, code, or sound, prompts act as the control mechanism. They help you guide what the model does, how it does it, and who it does it for.

Good prompting is not just about being precise. It’s also about being expressive and strategic. Sometimes breaking a complex task into steps helps. Other times, using creative or poetic language can guide the AI toward more imaginative results. Language is powerful, and how you use it shapes how the AI responds.

People with backgrounds in writing, art, history, or philosophy often excel at prompt engineering because they understand how language works at many levels. Their ability to express complex or nuanced ideas helps them craft prompts that produce more meaningful results.

One advanced method is meta-prompting, where you first ask the AI to design the prompts you will use for a complex, multi‑stage project. Instead of jumping into the work, you begin by co‑creating a plan of prompts that you’ll follow in sequence. This is especially useful when each step depends on the previous one.

For example, here’s how you might frame a meta‑prompt for a digital publishing workflow. First, you work with the AI to create a single reusable prompts that contains a logical sequence of prompts.

This format demonstrates four important points:

• Prompt‑for‑prompts: You begin by asking the AI to help you design the workflow first, giving you a reusable roadmap rather than a single one‑off answer.
• Process‑oriented prompts: A meta‑prompt doesn’t just request a single output; it defines an ongoing approach. By telling the AI to pause for approval before moving on, it shifts from “one‑and‑done” answers to a collaborative back‑and‑forth. That’s how you get better results—and keep control of the work.
• Logical sequencing: Clear step labels, goals, and “pause for approval” checkpoints keep the model from skipping ahead and make collaboration more reliable.

Segmenting the instructions with steps, headings, and short explanations also helps the AI follow the logic without skipping ahead. The model “sees” where each task begins and ends, which makes the whole workflow more reliable—and easier for you to review.

Here are some common prompting techniques that help you get the outputs you want:

Many AI tools today offer built-in prompt libraries or templates, making it easier for beginners to get started. These provide helpful starting points for everything from lesson planning to design ideation and marketing strategy.

Context Engineering goes beyond single prompts to shape the entire information environment. Context is what the model “knows,” “sees,” and “remembers” when it’s generating a response to a prompt. Context Engineering is about feeding the model the right knowledge, structuring that knowledge effectively, and managing how information flows over time. Done well, it makes AI output more reliable, relevant, and creative.

By combining prompting, meta-prompting and context engineering, you shift from giving instructions to designing environments for AI thinking. Prompts are the questions you ask; context is the stage you set for the answers.

Finally, remember that prompts and contexts don’t just shape the AI’s output—they also teach it about you. Every choice you make in wording, structure, and examples is a clue about how you think, write, and approach problems. By sharing your reasoning, style, and process in the prompt and context, you invite the model to mirror not just what you want, but how you would do it yourself.