• Dynamic per-label thresholds for large-scale search query classification with Otsu’s method

    Dynamic per-label thresholds for large-scale search query classification with Otsu’s method

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    Dan Petrovic
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    Solving the “Which Score Is Good Enough?” Puzzle The real-world problem Arbitrary label search-query intent classifiers spit out a confidence score per label.On clean demos you set one global cut-off say 0.50 and move on.In production: Manual tuning per label quickly turns into a never-ending whack-a-mole, especially when the taxonomy is customized client-by-client (e.g., SaaS…

  • Prompt Engineer’s Guide to Gemini Schemas

    Prompt Engineer’s Guide to Gemini Schemas

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    Dan Petrovic
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    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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    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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    Dan Petrovic
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    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

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    Dan Petrovic
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    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?

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    Dan Petrovic
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    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…

  • Universal Query Classifier

    Universal Query Classifier

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    Dan Petrovic
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    Generalist, Open‑Set Classification for Any Label Taxonomy We’ve developed a search query classifier that takes any list of labels you hand it at inference time and tells you which ones match each search query. No retraining, ever. Just swap in new labels as they appear. Old workflow Pain New workflow Build + label data + retrain…

  • Another failed attempt to kill SEO

    Another failed attempt to kill SEO

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    Dan Petrovic
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    If Marie Haynes, Barry Schwartz or Cindy Krum had written an article declaring SEO dead and proposing we rebrand our industry you’d seriously consider it. Wouldn’t you? What about Zach Cohen and Seema Amble? I don’t know either. Looked them up just now. Two VC people with insignificant footprint or long-term interest in SEO, Machine…

  • Vector Embedding Optimization

    Vector Embedding Optimization

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    Dan Petrovic
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    Embedding Methods Evaluation: Results, Key Findings, and a Surprising Insight On June 6, 2025, we ran a comprehensive evaluation comparing four different embedding methods—regular, binary, mrl, and mrl_binary—on a dataset of paired sentences. The goal was to measure each method’s speed, storage footprint, similarity quality, and accuracy against a ground-truth of sentence pairs. Below, we…

  • Dissecting Gemini’s Tokenizer and Token Scores

    Dissecting Gemini’s Tokenizer and Token Scores

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    Dan Petrovic
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    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…