Category: Google

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

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

    —

    by

    Dan Petrovic
    in , ,

    This article describes Google’s system for 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 for any input, even for queries it has never seen before. Primary Inputs List…

  • Analysis of Gemini Embed Task-Based Dimensionality Deltas

    Analysis of Gemini Embed Task-Based Dimensionality Deltas

    —

    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

    —

    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

    —

    by

    Dan Petrovic
    in , ,

    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

    —

    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…

  • Training a Query Fan-Out Model

    Training a Query Fan-Out Model

    —

    by

    Dan Petrovic
    in , ,

    Google discovered how to generate millions of high-quality query reformulations without human input by literally traversing the mathematical space between queries and their target documents. Here’s How it Works This generated 863,307 training examples for a query suggestion model (qsT5) that outperforms all existing baselines. Query Decoder + Latent Space Traversal Step 1: Build a…

  • Cosine Similarity or Dot Product?

    Cosine Similarity or Dot Product?

    —

    by

    Dan Petrovic
    in ,

    Google’s embedder uses dot product between normalized vectors which is computationally more efficient but mathematically equivalent to cosine similarity. How Googler’s work and think internally typically aligns with their open source code (Gemini -> Gemma) and Chrome is no exception. It’s why I look there for answers and clarity on Google’s machine learning approaches. After…

  • Dissecting Gemini’s Tokenizer and Token Scores

    Dissecting Gemini’s Tokenizer and Token Scores

    —

    by

    Dan Petrovic
    in ,

    As a technical SEO, you might be diving into machine learning (ML) to understand how tools like Google’s Gemini process text. One foundational concept is subword tokenization—breaking words into smaller pieces called “tokens.” While tokens themselves are context-agnostic (they don’t consider surrounding words), they do carry an inherent bias: each token’s likelihood reflects how prominent…

  • There’s a small army of on-device models coming to Chrome

    There’s a small army of on-device models coming to Chrome

    —

    by

    Dan Petrovic
    in ,

    1. ULM128M 2. LLMIT1B 3. GEMINI2_NANOV2 4. GEMINI2_NANOV2_EE2Q 5. GEMINI_XS 6. GEMINI_XS_DRAFTER_6LAYER_CAUSAL_USM_700M_RESIDUAL 7. GEMINI_XS_LUSM_700M_RESIDUAL_BOTTOM15 8. GEMINI2_NANOV2_EE12Q 9. GEMINI2_NANOV2_EE2_LUSM_700M 10. GEMINI2_NANOV2_CAUSAL_700M 11. GEMINI2_NANOV2_EE20_CAUSAL_LUSM_700M 12. GEMINI_XL_DRAFTER_24LAYER 13. GEMINI_XS_FA1 14. GEMMA2_8B 15. GEMMA2_7B 16. GEMMA2_2B 17. GEMMA3_1B 18. GEMMA3_4B 19. GEMMA3_12B 20. GEMMA3_27B 21. STABLELM_4E1T_3B_PHI_2_TF_LITE

  • Query Fan-Out Prompt Implementation in Google’s Open-Source Agentic Framework

    Query Fan-Out Prompt Implementation in Google’s Open-Source Agentic Framework

    —

    by

    Dan Petrovic
    in ,

    Google’s open-source “Gemini Fullstack LangGraph Quickstart” pairs Gemini 2.5 with LangGraph to showcase a fully transparent, citation-driven research agent (Mikami 2025). A React frontend (Vite, Tailwind CSS, Shadcn UI) collects user queries and displays progress, while a FastAPI/LangGraph backend orchestrates a multi-step workflow: Although this isn’t Google’s official Gemini implementation as seen in AI Mode…