Learning AI And SEO With NotebookLM: Key Terms & Techniques

This is a living blog (last updated: 29/06/2025) where I explore key concepts and terminology at the intersection of AI, SEO, and language models. I’m using Google’s NotebookLM to learn by building and refining conversations around each topic.

From query fan-out to transformers, these entries are designed to simplify complex ideas through real-world context. Scroll down to explore each topic - and check back as I update with new insights.

• All About The Query Fan-Out Technique

This episode primarily covers Google's AI Mode and its "query fan-out" technique, detailing how they differ from traditional search and their implications for search engine optimization (SEO).

• What Is Tokenization

This conversation comprehensively covers tokenization, primarily focusing on its definition, importance, types, algorithms, practical implementations, challenges, benefits, and applications in Large Language Models (LLMs) and AI.

• All About Machine Learning Embeddings

 This chat mainly covers what embeddings are, how they work, their importance in AI and machine learning, and their diverse applications across various data types like text, images, and audio.

• Understanding Attention Mechanisms

This conversation covers attention mechanisms in AI, detailing their fundamental workings, historical development, impact on various AI applications (especially NLP), and advantages over traditional models, while also touching upon their computational challenges and future directions.

• All About Word Vectors

This conversation fully explains the definition, types, properties, and mathematical operations of vectors.

• What is A Neural Network

This conversation covers the definition, architecture, function, training, types, and applications of neural networks within the broader fields of artificial intelligence and deep learning.

• What Are Transformers

This episode covers the various aspects of Transformer models and their significant impact on the field of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP).