A content model for generative search is the way a website's content is divided, structured, and interlinked so that systems can extract self-contained, usable answers from it. A classic website is built differently – around pages, keywords, and traffic. Generative search works with passages, entities, and questions, so the same content performs markedly worse within it, even when it is factually correct. The first layer of the model is the entity. The website must unambiguously answer the question of who you are, what you do, for whom, and how you differ, and do so consistently across all pages and external sources as well. Without a clear entity, the company gets confused with another one, its services are described imprecisely, and models prefer to cite someone they are certain about. The second layer is topics. Instead of articles on random topics, several thematic clusters are built in areas where the company has real experience. Each cluster has a core page that defines the topic, surrounded by pages dedicated to individual questions. The interlinking between them is not decoration – it is precisely what signals to systems which pages are related and what is central within a given cluster. The third layer is questions. For each topic, the specific questions people ask are mapped, including follow-up ones that come in the second and third steps of a conversation. The source is not only keyword research tools, but also questions from the sales and support teams, internal on-site search, and direct testing of models. Every question without a corresponding passage is a gap that the competition will fill. The fourth layer is the passage. This is the biggest change compared with common practice: the unit of optimisation stops being the page and becomes the paragraph. Every paragraph must make sense even when pulled out of context, meaning it must name the subject instead of using a pronoun, contain the complete claim including any figure or condition, and give the answer right in the first sentence. Above the paragraph sits a heading that corresponds to the question. The fifth layer is the form of the data. Parameters, prices, deadlines, and comparisons belong in tables, procedures in numbered steps, definitions in a single sentence. Figures should have the unit and the period of validity stated. Claims taken from elsewhere should have the source stated. This layer determines whether a passage can be used without interpretation, or whether the system has to fill in the gaps itself – and whatever it has to fill in, it would rather take from elsewhere. The sixth layer is maintenance. A content model is not a one-off rebuild. Data ages, questions change, and the competition fills in what you are missing. That is why the model includes a regular cycle: mapping new questions, updating data with the date stated, and merging pages that have started competing with each other. Switching to this model does not mean rewriting the website from scratch. In practice, it starts with an audit of existing pages, adding definition sentences and tables to the best-performing ones, and only then filling in the missing answers. The result also shows up in classic search, since it involves the same principles of clarity, just applied more rigorously. One more difference from common practice is worth mentioning. In classic SEO, content was planned according to search volume, so topics with few queries were not written about. In generative search the logic is different: a single user question breaks down into dozens of partial queries, and many of them are narrow and specific. It is precisely these narrow questions about terms, exceptions, limits, and edge cases that have low search volume, yet a high probability of appearing as part of a larger question. The content model therefore deliberately covers even topics that, based on search volume, would never make it onto the list. The second difference is the lifespan of the content: a page with specific figures requires regular review, otherwise the advantage turns into a burden.
See also: Content chunking, Keyword mapping to pages, Definition Sentence at the Beginning.
