A practical how-to or methodology.
The eCommerce Optimization Engine uses Google's multimodal embedding model to align product text with images to improve search retrieval and SEO performance.
We spent 2.71 Billion tokens teaching a machine to reverse-engineer how AI search ranks pages. What it found overturns the usual SEO playbook: structure and intent beat every credential you've been told to add, and the engine that proved it reached #1 in 1,554 of 2,246 runs.
LinkjeBERT is a Dutch language model trained on structured Markdown to predict where humans will place links in text using token-level confidence levels.
An analysis of Chrome's Page Content Agent, which builds a structured tree of page elements to provide Gemini with grounded context for reading and interaction.
An exploration of how standard embeddings can create a semantic soup by grouping search queries by adjectives rather than head nouns during clustering.
Explore prompt engineering techniques for SEO, including zero-shot, few-shot, role, and chain-of-thought prompting to improve content and automate tasks.
An overview of AI bots, distinguishing between training data scrapers used for LLM development and agentic bots designed for autonomous, goal-oriented tasks.
Semantic Similarity Rating (SSR) maps LLM free-text responses to Likert distributions to improve purchase intent realism and match human response patterns.
Identify AI-generated comments through statistical analysis of sentiment, formulaic linguistic patterns, repetitive vocabulary, and a lack of human imperfection.
Explore how to use Otsu's algorithm to solve the problem of inconsistent confidence thresholds in search-query intent classifiers using dynamic, per-label tuning.
A technical guide to the Gemini API GenerateContentResponse schema, detailing the structure of candidates, usage metadata, safety ratings, and parsed data.
Gemma-Embed is a bespoke 256-dim embedding model created by fine-tuning google/gemma-3-1b-pt with LoRA to enable high-fidelity query reformulation.
Google generates high-quality query reformulations by traversing the mathematical latent space between queries and documents to train the qsT5 model.
Google’s Gemini Fullstack LangGraph Quickstart uses Gemini 2.5 and LangGraph to build a citation-driven research agent with a React and FastAPI architecture.
This page explores mechanistic interpretability techniques, including activation logging, causal tracing through activation patching, and attention head analysis.
