Summary
Highlights
An introduction to the third class of the advanced SEO course. The focus includes LLMs, transformers, RAG systems, and vectors, emphasizing that computers use numbers, not language, to process information.
Explains the training phases of LLMs: pre-training on vast data, supervised fine-tuning (SFT) to learn Q&A patterns, and reinforcement learning from human feedback (RLHF) to ensure safety. LLMs are described as prediction engines that calculate word probabilities.
Discusses Google's 'Attention is All You Need' research, which revolutionized AI by using 'attention' to process context, reducing the massive computational power required to calculate word probabilities in large language libraries.
Explains vectorization as the process of converting content into mathematical scores based on various properties. Vector embeddings allow computer models to understand the 'closeness' of different concepts in a multi-dimensional space, which is essential for recommendation algorithms and semantic SEO.
Detailed breakdown of RAG systems like Perplexity. It covers how a RAG system uses a vector database and a live search engine to fetch information in real-time, combining it with an LLM to generate precise, up-to-date answers without requiring constant model retraining.
Summary of course progress and a look ahead at the next class, which will be a deep dive into using the Google Search Console for advanced SEO optimization.