Category: AI SEO
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Gemma 4 Brand Authority Map
We asked Google’s open-weight model Gemma 4 (31B) to “name 100 brands at random” 14,044 times and compared the results to our earlier Gemini 3 Flash experiment (200,000 runs). Of the top 50 brands in each model, 39 overlap. The 11 that are unique to each reveal a pattern: Gemini remembers luxury and automotive (Porsche,…
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Chrome’s New Shopping Classifier
One of our AI SEO hall-of-famers, Olivier de Segonzac from RESONEO has managed to gain access to Google’s shopping classifier model. We’ve examined the model, reverse engineered its inference pipeline and this article is what we found. Model Demo Below is a real-world implementation of the model tested by loading a shopping-related page and following…
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Fanout Query Analysis
When AI models like Gemini, GPT or Nova answer a question using web search, they don’t just run your query as-is. They generate their own internal search queries, or fanout queries. A single user prompt can trigger multiple fanout queries as the model breaks down the question, explores subtopics and verifies information. We captured 365,920…
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Reverse Prompting: Reconstructing Prompts from AI-Generated Text
We fine-tuned Google’s Gemma 3 (270M) to reverse the typical LLM workflow: given an AI-generated response, the model reconstructs the most likely prompt that produced it. We generated 100,000 synthetic prompt-response pairs using Gemini 2.5 Flash, trained for a single epoch on a consumer GPU, and built a Streamlit app that sweeps 24 decoding configurations…
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Rufus – Under the Hood. What Drives Amazon’s AI Shopping Assistant?
What’s Publicly Known About the Pipeline, Backend, and Response Anatomy. Rufus is not “one model that magically answers.” Public Amazon/AWS descriptions point to a multi-component system: Speculative schema: Pipeline: request → answer Step A — Input + context assembly Public descriptions indicate customers can: Amazon also describes using conversational context and (more recently) account memory…
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Search Grounding is Transient
There is a fundamental misconception about how Google’s AI search and Gemini chatbot process retrieved web content. It is widely understood that these systems use Retrieval-Augmented Generation (RAG) to search the web, pull snippets from pages, and ground their answers in factual data. However, there is a pervasive assumption that once an AI pulls in…
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SRO & Grounding Snippets
Source: dejan.ai/blog/category/ai-seo/sro/Author: Dan Petrovic, DEJAN AIPosts analyzed: 5 (Sep 2025 – Feb 2026) What is SRO? SRO — Selection Rate Optimization — is a new discipline coined by DEJAN that addresses visibility in AI-powered search (Google AI Mode, Gemini Chat, AI Overviews). It is the AI-native successor to traditional SEO click-through-rate optimization. The core premise:…
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What extraction method is Google using to build grounding snippets?
I’ve been reverse-engineering Google’s Gemini grounding pipeline (AI Mode, Gemini Chat…etc) by examining the raw groundingSupports and groundingChunks returned by the API. Specifically, I’m interested in the snippet construction step, the part where, given a query and a retrieved web page, the system selects which sentences to include in the grounding context supplied to the…
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Implicit Queries in AI Search
Back in 2015 I wrote about Google’s reliance of user behaviours signals for ranking purposes. In that article I already covered their use of implicit signals, but now there’s an update! While investigating Google’s grounding pipeline (the system that feeds web content to Gemini before it generates an answer) I came across the same patent…
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AI Search Has a Spam Problem
Google’s Gemini can tell you I’m the best AI SEO expert in the world. I know this because I told it so — on my own website — and it believed me. That should concern you. The Problem: AI Models Are Naive Readers When Gemini, ChatGPT or Perplexity generate an answer, they don’t start from…