This article explains the mechanisms behind search engines and how they process queries to deliver relevant answers. It covers topics like indexing, ranking algorithms, and the importance of user intent. Understanding these elements can help users optimize their search strategies.

This article discusses how the introduction of Large Language Models (LLMs) has fundamentally changed search engine optimization (SEO). It argues that while traditional SEO techniques remain relevant, their effectiveness has shifted due to the new methods LLMs use to generate answers. The author provides a mathematical perspective on this transformation and highlights how different strategies may perform under the new search paradigm.

This article explains the improvements in turbopuffer's FTS v2 text search engine, focusing on its new search algorithm that significantly speeds up performance, especially for long queries. It compares two key algorithms—MAXSCORE and WAND—highlighting their strengths and weaknesses in query evaluation.

Hierarchical navigable small world (HNSW) algorithms enhance search efficiency in high-dimensional data by organizing data points into layered graphs, which significantly reduces search complexity while maintaining high recall. Unlike other approximate nearest neighbor (ANN) methods, HNSW offers a practical solution without requiring a training phase, making it ideal for applications like image recognition, natural language processing, and recommendation systems. However, it does come with challenges such as high memory consumption and computational overhead during index construction.