Generative AI Fundamentals: Understanding Generative Models

Hijab e FatimaLast Updated on November 3, 2026

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Key Takeaways

Choose a predictive model for tasks with fixed labels and measurable accuracy, and generative AI when the output is new content that a person reviews.

• Traditional machine learning outputs a label or number that can be tested against known cases. Generative AI has no answer key.
• For one narrow task, a specialist model is usually more accurate and much cheaper than an LLM.
• Main risks are confident factual errors, biased output, deepfakes and misinformation at scale. Grounding answers in company documents and reviewing outputs reduces errors but does not remove them.
• Model rankings change every few months, while the four families change far more slowly, which makes the families the better starting point for a product decision.
• Arbisoft's view is that a first project should be internal and easy to check. Scale once the share of outputs passing review without edits holds steady.

Define what a good output looks like before building, then test specific prompts against that standard.

A team that wants software to write release notes needs a different model from a team that wants to flag fraudulent orders. Release notes call for generative AI, which learns patterns in existing data and produces new text, images, code or audio. Fraud flags call for a traditional predictive model that labels or forecasts. Picking the wrong one can cost a rebuild. These generative AI fundamentals explain how the four main model families work, how large language models reached their October 2026 state, where enterprises use them, the risks to plan for, and a simple rule for choosing between generative and predictive models for a first use case.

What is generative AI?

Generative AI is a branch of machine learning whose models learn the patterns in existing data and then produce new data that follows those patterns. A predictive model might forecast next month's sales or classify an email as spam. A generative model writes the email.

The output is a new sample built from what the model learned. That is why the same request can return two different answers, and why nobody can score a generated paragraph against one correct version. Teams judge it against a standard instead, such as factual accuracy and tone.

How does generative AI differ from traditional machine learning?

Traditional machine learning outputs a label or a number, such as "fraud" or a demand forecast. Generative AI outputs new content, such as a paragraph or an image. A fraud model can be tested against known fraud cases. A generated product description has no answer key.

Generative models are also more general. A sentiment analysis model predicts the emotional tone of text, but only within its predefined classes, and many businesses use sentiment analysis solutions of this kind to understand customer feedback. Give a large language model (LLM) a new prompt and it can classify the tone of a review and then draft a reply to the customer, all without retraining. For one narrow, well-defined task, though, the specialist model is usually more accurate and much cheaper to run.

The boundary is blurry in places. Some traditional techniques also produce data, such as super-resolution models that sharpen low-resolution photos, but each one does a single fixed job.

What are the main types of generative AI models?

Four model families cover most of what people mean by generative AI: generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models and transformer-based large language models. They differ in how they learn to generate and in the kind of content they handle best.

Generative adversarial networks (GANs)

A GAN trains two neural networks against each other. The generator creates samples. The discriminator estimates whether each sample came from the real training data or from the generator, and its verdicts teach the generator to produce samples that are harder to tell apart from real ones. The approach dates from a 2014 research paper.

For example, a GAN trained on thousands of photos of one product line can produce new product shots in the same style.

GANs fit narrow visual domains where sharp output matters. Training is the weak point. The two networks must stay balanced, and a generator that finds a few outputs the discriminator accepts can keep repeating them instead of covering the full variety of the training data.

Variational autoencoders (VAEs)

A VAE compresses each input into a short list of numbers, called a latent space, and learns a probability distribution over that space. To generate, it samples a point from the distribution and decodes it back into full data. The sampling adds controlled randomness. Outputs therefore resemble the training data without copying any single example.

For example, a VAE trained on normal machine sensor readings can generate realistic new readings for testing a monitoring dashboard.

VAEs fit tasks that need smooth, controllable variation of existing data. Their generated images are often softer and less detailed than GAN or diffusion output, which limits them for photorealistic work.

Diffusion models

A diffusion model learns to turn random noise into an image in many small steps. During training it sees real images gradually corrupted with noise and learns to reverse each step. To generate, it starts from pure noise and removes a little at a time until a coherent picture appears.

For example, a text prompt such as "a red bicycle against a brick wall" can guide every denoising step, and the picture that emerges matches the description.

Diffusion models fit high-quality image and video generation from text descriptions. Because generation runs through many steps, each output takes more computation than a GAN's single pass.

Transformer-based large language models

A transformer-based LLM generates text one token at a time, predicting the next token from everything that came before it. A token is a word or part of a word. The transformer design, introduced in the 2017 paper "Attention Is All You Need", relies on attention: when the model processes each token, it weighs every other token in the input by relevance.

In "The server crashed because it ran out of memory", attention links "it" to "server". Transformers also process a whole sequence in parallel, which made training on very large text collections practical. Our guide to transformer architecture components covers encoders, decoders and multi-head attention in more depth.

For example, an LLM can read a 20-page supplier contract and return a one-paragraph summary with every renewal date listed.

LLMs fit writing, summarizing, coding and question answering, and newer models also accept images as input. The main caveat comes from how they work. An LLM predicts plausible text, so it can state something false in the same confident tone as something true.

How did large language models evolve?

Large language models went from a research architecture to multimodal products in under a decade. The milestones below show two trends that matter to teams choosing a model: smaller models catching up with larger ones, and open-weight models closing in on closed ones.

• In June 2017, the transformer paper was published, and later LLMs such as GPT-4 are built on its design.
• In May 2020, OpenAI's GPT-3 arrived with 175 billion parameters, ten times more than any earlier non-sparse language model.
• In February 2023, Meta's LLaMA paper introduced models from 7B to 65B parameters and reported that LLaMA-13B outperforms the 175B GPT-3 on most benchmarks.
• In March 2023, OpenAI's GPT-4 arrived, accepting image and text inputs and producing text outputs.
• In July 2024, Meta released Llama 3.1, whose 405B version Meta called the first openly available model to rival the top AI models.
• In September 2026, OpenAI released GPT-6 Astra, which it describes as its most capable model for the most demanding work.

As of October 2026, Arbisoft's comparison of the best LLMs in 2026 lists GPT-6 Astra from OpenAI and Claude Fable 5.1 from Anthropic among the leading models, with Mistral Large 4, DeepSeek V4 Pro, Qwen3.6-27B and Muse Spark Open leading the open-weight group. Model rankings change every few months. The four model families above change far more slowly, which makes them the better starting point for a product decision.

How is conversational AI different from generative AI?

Conversational AI is the category of systems that hold a dialogue with people, such as chatbots and voice assistants. Generative AI is one technology those systems can use to write their replies. Older chatbots matched a user's request to a scripted dialogue tree and returned a fixed answer. An LLM-based assistant writes each reply from the conversation so far and can handle questions nobody scripted.

The price of that flexibility is control. A scripted bot never invents a refund policy. An LLM-based assistant can, unless its answers are grounded in approved documents and checked. Our view is that support bots, including AI chatbots for ecommerce customer support, work best as a mix: scripted flows for actions such as order changes and returns, and generated answers for open questions.

Why does prompt wording change generative AI output?

A prompt is the instruction a generative model works from, and every detail it leaves out is a detail the model has to guess. An effective prompt states the task, the context, the audience and the output format. Compare two prompts for the same job.

• Vague: "Write about our product."
• Specific: "Write a 100-word product description of a project-tracking app for engineering managers, in a neutral tone, as two short paragraphs."

The first prompt leaves the length and the reader open, and the result usually needs heavy rewriting. The second sets both, which gives a draft much closer to usable on the first attempt. The same principle holds for image and code prompts. Techniques such as iterative refinement are covered in our guide to crafting better prompts.

Where do enterprises use generative AI?

Enterprises get the most from generative AI where people already write first drafts of text or code and someone reviews them before use. Typical use cases include:

• Summarizing meeting notes and long email threads for internal teams
• Coding assistants that suggest code and explain errors
• Support chat that answers open questions from approved help-center content
• Image variations for marketing and design teams to review
• Synthetic test data when real customer records are too sensitive to copy

Arbisoft's own coding assistant is one example. The CodeKer case study describes an AI-powered alternative to platforms like Phind, GitHub Copilot and ChatGPT Plus, built in-house to raise the productivity of Arbisoft's engineers and to remove the risks of external paid tools while safeguarding proprietary code. The lesson for other teams is about data. When the input is confidential source code or customer records, where the model runs and who can see the prompts matter as much as output quality. For one specific enterprise setting, see our article on generative AI in enterprise learning management systems.

What risks does generative AI carry?

The main risks of generative AI are confident factual errors, biased output, deepfakes of real people and misinformation at scale. Each one needs a control before a model reaches customers.

Confident errors and missed context

An LLM can answer "Berlin" when asked for the capital of France, in the same tone it uses for correct answers. It can also miss context. Asked for a chocolate no-bake cake recipe, a model may return a baked cake recipe that matches the keywords and ignores the request. Some errors come from how models read text in tokens instead of letters, which explains why LLMs can't count the r's in strawberry. Grounding answers in a company's own documents and reviewing outputs both reduce these errors, but neither removes them.

Bias in training data and outputs

Generative models trained on data that reflects social biases can repeat those biases and sometimes amplify them. A support assistant trained mostly on conversations from one customer group, for example, may serve other groups less well. Diverse training data and fairness checks before release lower the risk, and our article on building responsible and fair machine learning models shows how to run those checks.

Deepfakes and misinformation

Generative models can produce realistic video and audio of real people. The US Government Accountability Office's 2020 Science and Tech Spotlight on deepfakes says they "can depict someone appearing to say or do something that they in fact never said or did" and calls them tools that can be used for exploitation and disinformation. Cheap, fast generation also makes fabricated text and images easier to spread. Before generative AI touches personal data or public-facing content, legal and security specialists should review the use case and the controls on its output.

When should you choose generative AI over traditional machine learning?

Choose a traditional predictive model when the task has fixed labels and accuracy can be measured against them, such as fraud flags or demand forecasts. A generative model is the better fit when the output is new content, such as drafts, summaries, code or images, and a person can review it before anyone relies on it. Many products need both. A support tool might use a classifier to route tickets and an LLM to draft the replies.

Our view is that a first generative AI project should be internal and easy to check, such as summarizing internal meeting notes. The team should define what a good output looks like before building, then test several specific prompts against that standard. A useful measure is the share of outputs that pass review without edits. Scaling makes sense once that share holds steady over a few weeks of real use.

Frequently asked questions

Can GPT-4 generate images?

OpenAI's GPT-4 cannot generate images. It accepts image and text inputs but produces text outputs, so it can describe a chart or answer questions about a photo without creating a new image. Image generation is handled by other model types, such as diffusion models and GANs.

How are diffusion models different from GANs?

A GAN learns through competition between a generator and a discriminator, and the generator improves each time the discriminator catches a fake. A diffusion model has no opponent. It learns to remove noise in small steps and builds an image gradually from random noise, which usually takes more computation per image than a GAN's single pass.

Why do LLMs make mistakes like miscounting letters in a word?

LLMs read text as tokens, which are whole words or word pieces, so the letters inside a word are often hidden from the model. Counting the r's in "strawberry" can trip it up for that reason. Tasks that need exact answers are better checked by code or a person.

Is ChatGPT generative AI or conversational AI?

ChatGPT is both. It is a chatbot, which makes it conversational AI, and the replies it writes come from generative large language models. Older scripted chatbots were conversational without being generative.

Do generative AI models need to be trained from scratch?

Training from scratch is rarely needed. In our view, most business use cases can start from a pre-trained model and adapt it through prompts, retrieval from company documents or fine-tuning. Reusing what a model learned in earlier training cuts computing costs, while training from scratch needs very large datasets and compute budgets.

Scoping a first generative AI use case

A feature that needs drafts or generated code is a candidate for generative AI, and the safest first version is small and reviewed by people against a clear standard. Arbisoft's generative AI development services include consulting, fine-tuning of pre-trained models and proof-of-concept builds, which suit a team that wants to test one use case before committing to a full product.