This episode explains the difference between tokens and embeddings, fundamental concepts in Artificial Intelligence (AI) and Large-Scale Language Models (LSMs) that impact SEO. Tokens are basic textual units, such as words, used in traditional keyword searching through sparse embeddings that consider only frequency. In contrast, dense embeddings are numerical representations that capture the semantic meaning and context of words, making them crucial for natural language understanding in modern AI systems. The article traces the evolution of Google search, highlighting how technologies like RankBrain, BERT, and MUM utilize embeddings to enhance the relevance of Search results. Finally, it presents hybrid search as a solution that combines the efficiency of semantic search with the accuracy of lexical (token-based) search, ensuring that AI systems can handle information both inside and outside their training domain.

In the Semantic SEO podcast, Alexander Rodrigues Silva, a Semantic SEO expert with over two decades of experience and a background in Library Science, presents AI-generated summaries of his articles. Discover how AI enhances insights into taxonomies, ontologies, and the search revolution, connecting the future of SEO to Information Science—an in-depth perspective on optimizing information on the Web.

This podcast is produced using Artificial Intelligence based on articles written on the Semantic Blog by me, Alexander Rodrigues Silva.