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Google’s Query Fan-Out System – A Technical Overview
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…
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GPT-5 System Prompt
Here it is: Credit to: https://x.com/elder_plinius/status/1953583554287562823H/T https://x.com/DarwinSantosNYC for spotting it.
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Journalism Is Dead. Say Hello to Gournalism.
John Botman For nearly two centuries, journalism operated under the assumption that truth mattered, stories should be original, and humans should write things for other humans to read. Quaint, right? We trusted journalists—those quirky creatures who collected facts, verified sources, and occasionally spelled words correctly—to give us nuanced, insightful accounts of the world. Oh, how…
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Human Friendly Content is AI Friendly Content
What do humans and AI have in common? We don’t read. Instead we rely on attention mechanisms to process text information. When optimising content for AI and humans you must get to the point early and optimise content to reduce cognitive load. Striking parallels in attention and information processing Transformers use attention mechanisms mathematically equivalent to…
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Analysis of Gemini Embed Task-Based Dimensionality Deltas
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…
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Dynamic per-label thresholds for large-scale search query classification with Otsu’s method
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…
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Prompt Engineer’s Guide to Gemini Schemas
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…
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Top 10 Most Recent Papers by MUVERA Authors
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…
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Training Gemma‑3‑1B Embedding Model with LoRA
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…
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Training a Query Fan-Out Model
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…
