Technical SEO, Advanced CPC and Digital Analytics Case Studies
If you’re still treating AI answers like “blue links with extra steps,” you’re going to miss where visibility actually happens. LLMs generate answer, they don’t rank or index anything. Then how do llms extract content and when do they quote it and link it? In browsing-enabled modes (ChatGPT w/ Bing, Bing Copilot, SGE, Perplexity, Claude), … Read more
Have you ever explored how llms work? If not, how can we talk of AEO and GEO and so on? Most marketing strategies today are still based on metaphors that no longer apply. Terms like “ranking,” “indexing,” and “domain authority” may be appropriate for search, but they have little meaning in the architecture of a … Read more
For over two decades, Search Engine Marketing strategies have revolved around one concept: ranking high in search engine results pages (SERPs) for the most valuable queries. To put it simply, and in this context deliberately not taking into account what happens after the click, success on the Search Marketing was measurable by clicks, impressions, and … Read more
Probably the most important misunderstanding in the search marketing industry today is not whether we should respond to the rise of generative AI. Response is inevitable. The real problem is that most teams are treating this shift as just another algorithm update, assuming these models behave like search engines. They don’t. This reaction is rooted … Read more
The foundation of any Large Language Model (LLM) lies in a process called pre-training. This is where the model learns how language works by processing an immense volume of human-generated text. Pre-training is self-supervised, non-interactive, and results in a static model: it defines what the model “knows”, and more importantly, what it doesn’t. Pre-training teaches … Read more
At the heart of every Large Language Model lies a special kind of neural network architecture called the transformer. Originally designed for natural language processing, transformers have since become the foundational architecture across AI domains, powering not only text generation, but also image recognition, video understanding, audio synthesis, and multimodal reasoning. But before we explore … Read more
While pre-training equips Large Language Models (LLMs) with a broad statistical understanding of language, it does not make them helpful, safe, or aligned with user expectations. Left in their raw form, these models can be verbose, biased, evasive, or simply unhelpful, even when technically accurate. To bridge the gap between linguistic fluency and user alignment, … Read more
Despite their remarkable fluency, Large Language Models (LLMs) don’t “know” anything in the human sense of the word. They do not reason with will or identity. They do not retrieve. They do not store facts in a database. What they do is predict, based on statistical patterns. There is no will. There is no “intelligence” … Read more
So far, we’ve explored the core building blocks that allow Large Language Models to process and predict language: self-attention, positional encoding, and embeddings. Now, we’ll look at how these components are arranged inside a transformer model and how this architecture enables emergent capabilities like reasoning, abstraction, and memory-like (I emphasize, “like”) behavior. This is where … Read more
In this research, I’ll try to address the SEO for AI topic by explaining how AI models find and select web content, and how you can optimize your site to become the source that AI references. Generative AI models and LLMs like ChatGPT are becoming a new layer in content discovery, and have partially mangled … Read more