How Generative AI Actually Works — LLMs, GPTs, and Diffusion Models Explained

How Generative AI Actually Works

You do not need a PhD in machine learning to use AI tools effectively — but understanding the basics of how they work will make you a dramatically better user. When you understand why a language model sometimes "hallucinates" facts, why image generators struggle with hands, or why some prompts produce brilliant results while others fall flat, you move from being a passive user to an informed collaborator.

This lesson explains the core technologies behind generative AI using clear analogies and practical context. By the end, you will understand how large language models generate text, how diffusion models create images, and how this knowledge directly improves the results you get from every AI tool you use.

The Simple Explanation of Large Language Models

A Large Language Model (LLM) is the technology behind tools like ChatGPT, Claude, and Gemini. At its core, an LLM does one thing extraordinarily well: it predicts what word should come next in a sequence.

Here is the analogy that makes it click: imagine you have read every book, every website, every article, and every conversation ever written in English. You have absorbed millions of patterns about how humans communicate — how sentences are structured, how arguments are built, how questions are answered, how stories unfold, how code follows syntax rules, how medical diagnoses are described.

Now someone gives you the beginning of a sentence: "The capital of France is..." and you predict the next word. With all that reading behind you, you confidently predict "Paris." Not because you have visited France, not because you "know" geography in a human sense, but because in the millions of texts you have absorbed, "The capital of France is Paris" appeared thousands of times in thousands of contexts.

This is fundamentally what an LLM does — but at a scale and sophistication that produces results that look and feel like genuine understanding. When you ask ChatGPT to write a marketing email, it is drawing on patterns from millions of marketing emails it encountered during training. When you ask Claude to debug your Python code, it is drawing on patterns from millions of code examples and programming discussions.

The key insight: LLMs do not "understand" in the human sense — they recognize and reproduce patterns at superhuman scale. This distinction matters because it explains both their remarkable capabilities and their characteristic limitations.

How Training Works — From Web Data to Intelligence

The training process for an LLM happens in several stages:

Stage 1: Pre-training on massive data. The model is exposed to enormous amounts of text — books, websites, academic papers, code repositories, forums, news articles. For GPT-4, this training data is estimated at over 13 trillion tokens (roughly 10 trillion words). During pre-training, the model learns statistical relationships between words, concepts, and patterns. It learns grammar, facts, reasoning patterns, coding syntax, and conversational norms — all from the patterns in the data.

Stage 2: Fine-tuning with human feedback (RLHF). After pre-training, human evaluators rate the model's responses. They tell the model which answers are helpful, accurate, and safe versus which are unhelpful, incorrect, or harmful. The model adjusts its behavior based on this feedback. This stage is what transforms a raw text predictor into a useful assistant that follows instructions, admits uncertainty, and refuses harmful requests.

Stage 3: Instruction tuning and alignment. Additional training teaches the model to follow specific instructions, maintain a consistent persona, handle complex multi-step tasks, and adhere to safety guidelines. This is where the "personality" and capabilities of tools like ChatGPT or Claude are refined.

Stage 4: Ongoing updates. Models receive periodic updates — new training data, improved fine-tuning, expanded capabilities — which is why ChatGPT and Claude continue to improve over time.

The entire process requires extraordinary computational resources. Training a frontier model like GPT-4 or Claude Opus is estimated to cost between $50 million and $200 million in compute alone. This is why only a handful of organizations — OpenAI, Anthropic, Google, Meta — can develop frontier models.

The Model Families — A Practical Comparison

Understanding the major model families helps you choose the right tool for each task:

No single model family is "best" at everything. GPT models tend to excel at creative tasks and have the largest user base. Claude models are known for careful, nuanced analysis and handling very long documents. Gemini models integrate seamlessly with Google's ecosystem. Llama and Mistral models can be run locally for privacy-sensitive applications.

As a practical user, your best strategy is to become proficient with two or three model families so you can choose the right tool for each task.

How Image Generation Works — Diffusion Models Explained

While LLMs generate text by predicting the next word, diffusion models generate images through a fundamentally different process. Tools like DALL-E, Midjourney, and Stable Diffusion all use variations of this approach.

Here is the analogy: imagine you take a photograph and gradually add random noise to it — like static on a television — until the image is completely unrecognizable. Now imagine you train a neural network to reverse this process: given a noisy image, predict what the slightly-less-noisy version looks like. Do this enough times, starting from pure noise, and you can generate a completely new image from nothing.

The process works like this:

1. Training: The model is shown millions of images paired with text descriptions. It learns the relationship between text descriptions and visual features — what "a golden retriever on a beach at sunset" looks like versus "a cat sitting on a windowsill in the rain."

2. Generation: When you type a prompt, the model starts with pure random noise and iteratively "denoises" it, guided by your text description, until a coherent image emerges. Each step makes the image slightly clearer and more aligned with your prompt.

3. Refinement: The number of denoising steps, the guidance scale (how closely to follow your prompt versus allowing creative freedom), and the random seed all affect the final result. This is why the same prompt can produce different images each time.

The text-to-image pipeline means that your prompt directly controls the output. Vague prompts like "a dog" produce generic images. Detailed prompts like "a golden retriever puppy sitting in a field of wildflowers, soft afternoon light, shallow depth of field, professional pet photography" produce dramatically better results. This is why prompt engineering matters just as much for image generation as for text generation.

The AI Terminology Hierarchy — Clearing Up Confusion

The terms AI, machine learning, deep learning, generative AI, and AGI are often used interchangeably, but they refer to different things. Understanding the hierarchy eliminates confusion:

• Artificial Intelligence (AI): The broadest term. Any computer system that performs tasks normally requiring human intelligence — from a chess engine to a spam filter to ChatGPT. AI has existed since the 1950s.

• Machine Learning (ML): A subset of AI. Systems that learn from data rather than being explicitly programmed. Instead of writing rules like "if the email contains 'free money,' mark it as spam," you feed the system thousands of emails labeled as spam or not-spam and it learns the patterns itself. ML has been mainstream since the 2000s.

• Deep Learning: A subset of ML that uses neural networks with many layers (hence "deep"). These architectures can learn incredibly complex patterns — recognizing faces in photos, transcribing speech, translating languages. Deep learning is the breakthrough that made modern AI possible, gaining mainstream adoption around 2012.

• Generative AI: A subset of deep learning focused specifically on creating new content — text, images, audio, video, code. This is what ChatGPT, DALL-E, Midjourney, and similar tools do. Generative AI became mainstream in late 2022 with ChatGPT's launch.

• AGI (Artificial General Intelligence): A theoretical future AI that can perform any intellectual task a human can do, with the ability to learn and adapt across domains without specific training. AGI does not exist yet. Current AI systems, no matter how impressive, are "narrow" — they excel at specific tasks but cannot generalize the way humans do.

When people say "AI" in 2026, they almost always mean generative AI — the tools that create text, images, and other content. This course focuses on generative AI because it is the category of AI that is most immediately useful and accessible to professionals.

Why Understanding the Basics Makes You Better

This technical knowledge is not academic — it directly improves how you use AI tools:

• You understand why prompts matter. Since LLMs predict based on patterns in training data, your prompt is the signal that activates relevant patterns. A better prompt activates better patterns and produces better output.
• You understand hallucinations. LLMs generate plausible-sounding text based on patterns, but they do not verify facts against a database. When the patterns suggest a confident answer but the training data was incomplete, the model "hallucinates" — producing text that sounds authoritative but is factually wrong. Knowing this, you always verify critical facts.
• You understand model selection. Different models were trained on different data with different objectives. Choosing the right model for your task — Claude for careful analysis, GPT-4o for creative brainstorming, Gemini for web-integrated research — produces better results than using one model for everything.
• You understand image prompt engineering. Knowing that diffusion models work from text-to-visual-feature mappings explains why specific, descriptive prompts produce dramatically better images than vague ones.
• You can evaluate new tools intelligently. When a new AI tool launches, you can assess whether its underlying technology is suited to your needs, rather than relying on marketing claims.

Key Takeaways

• LLMs work by predicting the next word in a sequence, drawing on patterns learned from trillions of words of training data — they do not "understand" in the human sense but produce remarkably useful results
• Training happens in stages: pre-training on massive data, fine-tuning with human feedback, instruction tuning, and ongoing updates — this process costs tens to hundreds of millions of dollars
• Different model families have different strengths: GPT for versatility, Claude for analysis and nuance, Gemini for Google integration, Llama and Mistral for open-source flexibility
• Diffusion models generate images by starting from noise and iteratively refining based on your text prompt — detailed prompts produce dramatically better images
• The AI terminology hierarchy moves from broad to specific: AI > Machine Learning > Deep Learning > Generative AI, with AGI remaining theoretical
• Understanding the basics makes you a better user — you write better prompts, choose better tools, anticipate limitations, and evaluate outputs more effectively
• You do not need technical expertise to use AI well, but foundational understanding transforms you from a passive user into an informed collaborator