Technical SEO, Advanced CPC and Digital Analytics Case Studies
When an AI-generated answer uses information from your website, what did the system actually see? The intuitive explanation is that an assistant finds the page, reads it and quotes the relevant part. In practice, LLM content extraction can involve several intermediate representations: search-result summaries, parsed documents, retrieved passages, reranked candidates and a final context assembled … Read more
Large Language Models are now embedded in search, content workflows, analytics tools and marketing platforms. But before asking how to optimize for them, influence them or measure visibility inside them, there is a more basic question we need to answer: What is the system actually doing? That question sounds obvious, but many of the expressions … Read more
Search marketing has always connected an information need with a discoverable result and, ideally, a useful next action. What is changing is where some of the reading, comparison and evaluation now happens. On a conventional search results page, the user sees a list of links, chooses a source and then starts evaluating what it says. … Read more
“Can we influence ChatGPT?” sounds like a practical question, but for a marketing team it is still far too broad. Influence what, exactly? A single answer? Whether a product page can be retrieved? How often a brand is cited across a defined set of prompts? Or what a custom assistant can access and use? These … Read more
The foundation of a Large Language Model begins with pre-training: the computationally intensive stage in which the model learns statistical structure from a very large collection of examples. The important word here is learns, because it is easy to give it a human meaning it does not have. During pre-training, the model is not reading … Read more
What happens between a sentence entering a language model and the model producing an answer? At the transformer level, three ideas do much of the explanatory work: numerical representations, attention and sequence structure. Together they help explain how information in the available context can interact while a model computes its next output. For marketers, this … Read more
Pre-training gives a language model a broad ability to continue text, represent patterns and perform many tasks. But that alone does not make it behave like the assistant a user expects. A user usually wants something more specific: follow the instruction, respect the requested format, preserve important conditions, avoid unsupported claims and communicate uncertainty when … Read more
Large language models create an uncomfortable combination: they can answer difficult questions remarkably well and still make elementary factual mistakes. The easy explanation is to say that LLMs “do not really know anything”. But that statement is too blunt to be useful. If a system correctly compares two products, follows a technical specification or solves … Read more
The previous articles introduced pre-training and embeddings, positional information and attention. Now we can put those pieces together. What actually happens between token representations entering a language model and the model producing scores for the next token? The answer matters because transformer architecture attracts unusually convenient marketing myths. Attention becomes “make these words bold”. Learned … Read more
What does it actually mean to optimize a website for AI? There are several different questions hidden inside that phrase. Can the relevant system discover the page? Can it retrieve the information needed for a particular question? Does the representation preserve the product, version and conditions that make the answer correct? Does the generated answer … Read more