Generative AI

Older AI mostly sorted or predicted: is this email spam, will this customer cancel. Generative AI produces something new, like a paragraph, a product photo or a melody. Text tools run on large language models; many image tools run on diffusion models.

"New" does not mean original in the human sense. The output is a fresh combination of learned patterns, which is why it can be fluent and still wrong, generic or uncomfortably close to its sources. Treat every output as a first draft from a fast assistant who has read a great deal but checked nothing.

How it works

A generative model is trained on a huge collection of examples until it learns what plausible content looks like. When you give it a prompt, it builds an output step by step: a text model predicts the next token again and again, while an image model starts from random noise and refines it into a picture that matches the description.

Because each step involves some controlled randomness, the same prompt can give different results each time. Output quality depends heavily on what you give the model. A one-line request returns average, generic text. A request that includes your audience, examples of your own writing, the facts to use and the length you want returns something far closer to usable. The model has no memory of your business between separate chats unless the tool stores it, so the context has to come from you each time or from documents connected to the tool.

Generative models do not look things up by default. They produce what is statistically likely, which is why they can state wrong facts with confidence, a failure known as hallucination. Many tools add a retrieval step that searches the web or a company's documents first and writes the answer from what it found. That improves accuracy on current facts but does not remove the need to check, because the model can still misread or misquote its sources.

Example

A landscaping company writes a short description of a finished garden and asks a text tool for three versions of a social media caption. It then asks an image tool for a header illustration for its seasonal newsletter. Both outputs take seconds; both still need someone to check facts, tone and that the image does not mislead customers about real work.

A sensible way to test a tool before relying on it is to ask it about something you know thoroughly, such as your own services, opening hours and service area. Wherever it gets details wrong, you have learned both how far to trust it and which facts about your business are unclear or missing online. Keep the questions and the dates you asked them, because answers change as tools and their sources are updated.

Why it matters for your business

Generative AI cuts the time it takes to produce drafts, variations and first versions, which helps small teams. The risks are real: invented facts, bland copy that sounds like everyone else's, and images that could mislead. It also powers the AI answer engines that now summarise businesses for customers, so it affects how you are found as well as how you work.

Common mistakes

• Publishing generated text without checking every fact, name and number in it.
• Using generated images to depict real products, premises or results you cannot actually deliver.
• Expecting a distinctive brand voice from a one-line prompt with no examples or guidance.
• Pasting confidential customer data into a public tool without checking how that data is stored and used.

Questions people ask

What is the difference between generative AI and AI?

Generative AI is one branch of AI. AI as a whole includes systems that classify, predict or recommend, such as fraud detection or spam filters. Generative AI specifically produces new content like text, images or audio. Chat assistants and image generators are the best-known examples.

Can I use generative AI content commercially?

Often yes, but it depends on the tool's terms and your country's law, which is still developing. Check each provider's terms of use, avoid prompts that copy a named artist or brand, and be aware that purely machine-made content may not get the same copyright protection as human work in some jurisdictions.

Does Google penalise AI-generated content?

No, not for being AI-made. Google says it judges the quality of content rather than how it was produced, and that using automation, including AI, mainly to manipulate rankings breaks its spam policies. AI-written pages that add nothing new, repeat what is already online or contain errors tend to perform poorly. Edited, fact-checked content with real expertise behind it is treated like any other page.