Category: Google

  • EmbeddingGemma: The Game-Changing Model Every SEO Professional Needs to Know

    EmbeddingGemma: The Game-Changing Model Every SEO Professional Needs to Know

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    by

    Dan Petrovic
    in , ,

    Why Google’s Latest Embedding Model Could Reshape Search Understanding In the business of Gen AI search optimization, staying ahead means understanding the underlying technologies that power modern search systems. Today, Google has released EmbeddingGemma, a ground-breaking multilingual embedding model that represents a key piece of the puzzle for anyone serious about understanding how Google processes…

  • Primary Bias on Selection Rate in AI Search

    Primary Bias on Selection Rate in AI Search

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    by

    Dan Petrovic
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    What is Selection Rate? Selection Rate (SR) is a key performance metric for AI systems that measures the frequency with which an AI selects and incorporates a specific item from a total set of grounding results. It serves as the Gen AI-native equivalent of Click-Through Rate (CTR) in traditional digital interfaces. SR = (Number of…

  • What is “Help Me Write” in Chrome?

    What is “Help Me Write” in Chrome?

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    by

    Dan Petrovic
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    Introduction “Help Me Write” is Google Chrome’s AI-powered writing assistant designed to help users create short-form content directly within their web browser. Launched with Chrome version 121, this feature leverages artificial intelligence to generate text suggestions based on user prompts and the context of the webpage you’re viewing. How Does Help Me Write Work? The…

  • Your website is about to start talking. Are you ready for this?

    Your website is about to start talking. Are you ready for this?

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    by

    Dan Petrovic
    in ,

    Chrome is about to give all websites a voice through a built-in version of Gemini. Your visitors will have completely private chats with it. No external API calls to Google’s servers and once loaded you can even switch off the internet – it will still work! What will they talk about? The Silent Web is…

  • Inside Chrome’s Semantic Engine: A Technical Analysis of History Embeddings

    Inside Chrome’s Semantic Engine: A Technical Analysis of History Embeddings

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    by

    Dan Petrovic
    in

    I decoded Chrome’s internal semantic search, found the exact chunking mechanism, embedding logic and am now able to browse, search and cluster my own search history through decoded vector embeddings. This is an in-depth technical analysis of Chrome’s history embeddings system based on Chromium source code and official Google documentation. Google Chrome has implemented a…

  • Google’s Query Fan-Out System – A Technical Overview

    Google’s Query Fan-Out System – A Technical Overview

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    by

    Dan Petrovic
    in , ,

    We have successfully replicated Google’s query fan-out approach following their research papers and this article describes the exact mechanics of automatically generating multiple intelligent variations of search queries using a trained generative neural network model. Unlike traditional systems that rely on pre-defined rules or historical query pairs, this system can actively produce new query variants…

  • Analysis of Gemini Embed Task-Based Dimensionality Deltas

    Analysis of Gemini Embed Task-Based Dimensionality Deltas

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    by

    Dan Petrovic
    in , ,

    When generating vector embeddings for your text using Gemini Embed there are several embedding optimisation modes: For each one you get slightly different embeddings, each optimised for the task at hand. The embeddings for semantic similarity are the most unique from all other types while retrieval query, retrieval document and fact verification embeddings are most…

  • Prompt Engineer’s Guide to Gemini Schemas

    Prompt Engineer’s Guide to Gemini Schemas

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    by

    Dan Petrovic
    in ,

    Prompt Engineer’s Guide to Gemini API GenerateContentResponse Schemas This guide provides a comprehensive and technical deep dive into the GenerateContentResponse schema, which is the primary output structure for the Gemini API’s GenerateContent method. Understanding this schema is crucial for effectively parsing, interpreting, and utilizing the responses generated by the Gemini model. 1. Overview/Summary The GenerateContentResponse…

  • Top 10 Most Recent Papers by MUVERA Authors

    Top 10 Most Recent Papers by MUVERA Authors

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    by

    Dan Petrovic
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    MUVERA Authors: 1. Laxman Dhulipala (Google Research & UMD) Top 10 Recent Papers (2023-2025) Research Focus Areas 2. Majid Hadian (Google DeepMind) Top 10 Recent Papers (2023-2025) Research Focus Areas 3. Jason Lee (Google Research & UC Berkeley) Top 10 Recent Papers (2023-2025) Research Focus Areas 4. Rajesh Jayaram (Google Research) Top 10 Recent Papers…

  • Training Gemma‑3‑1B Embedding Model with LoRA

    Training Gemma‑3‑1B Embedding Model with LoRA

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    by

    Dan Petrovic
    in , ,

    In our previous post, Training a Query Fan-Out Model, we demonstrated how to generate millions of high-quality query reformulations without human labelling, by navigating the embedding space between a seed query and its target document and then decoding each intermediate vector back into text using a trained query decoder. That decoder’s success critically depends on…