What is generative AI? Examples of its models and how they are used

Generative AI is the branch of artificial intelligence that creates new content, such as text, images, audio, video or code, after learning patterns from very large amounts of data. One well-known example of a generative AI model is GPT, the family behind ChatGPT; others include Google's Gemini, Anthropic's Claude and ALLaM, developed in Saudi Arabia.

This guide explains what generative AI is, how a model produces a sentence or a picture, which models exist for each type of content, and where a person still has to check the result. Model names were read on the vendors' own pages on 14 October 2026; they change often, so treat versions as a snapshot. O AI is not affiliated with OpenAI or any other company named here.

What is generative AI, and what makes it different?

The Saudi Data and AI Authority (SDAIA) defines a generative model in its 2025 guidelines for the public as a machine learning model that can create new examples similar to the data it was trained on. It describes generative AI as the part of AI that creates new content, including text, images, sounds, icons and videos, and works by interpreting the commands users give it [1].

The US National Institute of Standards and Technology (NIST) uses a close definition in its Generative AI Profile of July 2024: a class of AI models that mimic the structure and characteristics of their input data to generate derived synthetic content, including images, videos, audio and text [2]. SDAIA's AI Adoption Framework places generative AI alongside machine learning, deep learning, language processing, computer vision and speech technology as one branch of AI, and calls it one of the fastest-growing [3].

The key word is new. Most of the AI that businesses used before these tools spread sorted, scored or forecast things. Generative AI writes, draws or speaks something that did not exist a second earlier.

How a generative AI model produces text, images and sound

You do not need the mathematics to use these tools well, but three ideas explain most of their strengths and their mistakes.

It learns patterns from huge amounts of data

A model is first trained on a very large collection of examples. The scale is hard to picture: SDAIA's National Center for AI says the 7-billion-parameter version of its ALLaM model was trained on 5.2 trillion tokens, 4 trillion in English followed by 1.2 trillion of mixed Arabic and English [4]. A token is a short piece of text, often part of a word. The model does not store these texts like a library; it learns the patterns in them.

Text models predict the next piece

NIST explains that generative models produce outputs that approximate the statistical distribution of their training data; large language models (LLMs) predict the next token or word in a sentence [2]. Most of today's LLMs build on the Transformer, an architecture introduced in a 2017 research paper that relies on a mechanism called attention to weigh how each word relates to the others [5].

This is why a chatbot can write a polite Arabic letter in seconds, and also why it can invent a regulation number that looks right. It is producing the most likely continuation, not looking up a fact.

Image models remove noise step by step

Many image generators use diffusion models. They start from random noise and clean it up in a sequence of small denoising steps until a picture appears; a 2022 paper made this cheaper by running the process in a compressed "latent" space and by letting text guide each step [6].

Tuning turns a raw model into an assistant

After the first training stage, developers tune the model on examples of good answers and on human preferences. The ALLaM research team reports that extensive alignment with human preferences improved its models' results, even against larger models with weaker alignment [7]. Your instructions, or prompts, then steer the tuned model for each task.

Examples of generative AI models by type of content

The table groups well-known models by what they create. The model families last longer than their version numbers, so the families are what matter when you compare.

On 14 October 2026, OpenAI listed its GPT-6 family as its flagship models [8], and Anthropic's current line-up was Claude Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5, all accepting text and image input and producing text [10]. Many of these models also write and review code, which is a text task with stricter rules.

Is ChatGPT a generative AI model? Apps, models and companies

Many people meet the question "give one example of a generative AI model" in a course or a quiz. The cleanest answer separates three layers:

ChatGPT is an application; the generative AI model inside it is GPT. Either answer is usually accepted, and so are Gemini, Claude, ALLaM or an image model such as GPT-Image. A spam filter or a credit-scoring model is not an example, because it classifies rather than creates.

The distinction also matters when you buy. Two apps can run the same model, and one app can switch models between versions, so the quality you saw in a demo may change. Ask which model and version a product uses, and test it again when that changes.

Arabic generative AI built in Saudi Arabia

Arabic needs models that handle its script, its rich grammar and its dialects well. ALLaM, developed by the National Center for Artificial Intelligence at SDAIA, is a series of models for Arabic and English [4]. Its research paper describes teaching an English-capable model Arabic as a second language, by expanding its vocabulary and training on mixed Arabic and English text, without losing its English; the authors report leading results on several Arabic benchmarks, including Arabic MMLU and Arabic Exams [7].

HUMAIN, a company owned by the Public Investment Fund, launched HUMAIN Chat on 25 August 2025. Saudipedia describes it as an Arabic chat app powered by ALLaM 34B that accepts voice input, supports several Arabic dialects, switches between Arabic and English in one conversation and is hosted on HUMAIN's infrastructure inside the Kingdom [11].

Hosting location matters for companies handling personal data. Our guide to data residency in Saudi Arabia explains what the Personal Data Protection Law asks when an AI tool runs abroad.

How Saudi businesses use generative AI: illustrative examples

SDAIA expects generative AI to raise efficiency by taking on routine work such as summarising complex documents, transcribing text and generating images and videos, and to improve accessibility by producing content that fits local dialects [1]. In a company, that usually means a first draft that a person finishes.

Illustrative examples. The businesses below are hypothetical; they show where the technology fits and what stays with people.

The pattern repeats: the model produces a quick first draft of a text-heavy task, and a person who knows the subject checks and finishes it. Our guide on how AI can help your business shows how to pick one such task and measure it in a short trial before you pay for anything.

Where generative AI goes wrong: confident errors, data leaks and deepfakes

NIST lists "confabulation" among the main risks of generative AI: the production of confidently stated but false content, known informally as hallucinations [2]. It notes that this is a natural result of how the models are designed, and that models may also produce invented citations or reasoning that make a wrong answer look justified [2]. A 2025 research paper argues that models guess because their training and evaluation reward guessing over admitting uncertainty [12].

SDAIA's public guidelines add the risks a business meets first [1]:

• Misinformation: users are responsible for verifying generated content against trustworthy sources.
• Data leaks: prompts may be used to train the model further, so sensitive information typed into a tool can be exposed; organisations should ban classified information from third-party tools.
• Deepfakes: realistic fake audio, images and video are used for scams, fraud and blackmail; staff need training to spot them and to confirm requests through a second, trusted channel.

For government entities the line is stricter. SDAIA's guidelines for government tell employees not to enter data classified as restricted or higher, and to limit generative AI tools to data classified as public [13]. NIST also flags performance gaps between languages, possibly caused by non-representative training data [2]. Arabic dialects, names and Hijri dates are where those gaps tend to show.

Eight checks before you rely on generative AI output

Use this list as-is for any draft that will leave your desk.

1. Give it the source. Paste or upload the document you want summarised instead of asking from memory.
2. Ask where each claim comes from, then open every link or page it cites yourself.
3. Check names, numbers and dates against the original, including Hijri and Gregorian dates.
4. Check every legal or regulatory reference on the official portal; never cite an article number the tool supplied without reading it.
5. Keep personal and confidential data out of tools whose data terms and location you have not checked.
6. Label AI-generated content where readers could mistake it for a person's work; SDAIA's transparency principle asks for this [1].
7. Keep a person's sign-off on anything sent to a customer, a court or a government entity.
8. Note the tool and model version used for important outputs, so you can repeat or explain them later.

Your next step

Choose one recurring writing or summarising task this week, run the same real example through two generative AI tools your company is allowed to use, and count the corrections each draft needs before it could be sent. That count tells you more about what generative AI can do for you than any ranking. If you later need a model connected to your own documents or systems, that is a software project; you can read about O AI, a software and AI company in Al Khobar, on our home page.