Generative AI

Generative AI is the family of machine learning models that produce new content, such as text, images, audio, video or code, in response to a prompt. Chatbots like ChatGPT, Gemini and Claude are the best-known examples, built on large language models. Like any other machine learning model, a generative model is making predictions: it predicts the most plausible response based on the data it was trained on.

How it works

A generative model is first trained on a very large collection of examples (for text models, a large share of the public web, books and code) so it learns statistical patterns in that data. Most text models today use the Transformer architecture. When you send a prompt, the model generates its answer one token at a time, each time choosing a likely next token given everything before it. Image and audio models follow the same idea with different data and architectures.

Generative AI sits inside deep learning, which sits inside machine learning and the wider field of artificial intelligence. MLforSEO’s framing is that it is a small, very visible subset of AI: it gets most of the attention on social media, yet most production ML in search and marketing is classification, clustering, extraction and ranking.

How it’s used in SEO and marketing

• Content transformation: summarising, rewriting, translating and repurposing content from one format to another, which is where generative models are strongest.
• Drafting meta descriptions, alt text and product copy for a human to review and approve.
• Question answering and chat interfaces on top of your own content.
• Powering AI search features such as Google’s AI Overviews and AI Mode, which is why “being visible to generative AI” is now an SEO concern in its own right.

Example

MLforSEO compared purpose-built NLP APIs with generative AI chatbots on the same entity extraction task, using one simple zero-shot prompt. Google Cloud’s Natural Language API returned the most unique entities (59, recounted). DeepSeek R1 led the generative group with 37, Gemini returned nothing because it read the text as a question about a person, and a chatbot gave a different answer when the same prompt was run again. Only the APIs returned confidence, salience or knowledge-base links. The conclusion: generative AI is excellent at content transformation and the wrong instrument for repeatable, structured text analysis.

Limitations and things to watch

• Outputs vary between runs, which makes them hard to replicate and audit. Temperature settings reduce this without removing it.
• Models can state wrong facts confidently (hallucination). Keep them away from values that must be exact, such as prices, dates and authors in structured data.
• At scale, general-purpose models are usually slower and more expensive per item than a task-specific model or API.
• Google’s guidance (updated October 2026) allows AI-generated content that is accurate and helpful, but generating many pages without adding value can breach its scaled content abuse spam policy. Fact-check every output before publishing.

Learn it on MLforSEO

• Comparing NLP APIs for Entity and Sentiment Analysis (vs Generative AI) tests chatbots against purpose-built APIs.
• How to Automatically Generate Meta Descriptions with Python: From BERT to LLMs shows generative models used with guardrails.
• Structured Data Automation with Python explains where to keep LLMs away from your data.
• Introduction to Machine Learning for SEO covers the difference between AI and ML and the content transformation module.