What is AI Search? The Evolution from Keywords to Vector Search & RAG
AI Summary
The video explains how AI search is transforming the way we find and consume information online, evolving beyond traditional keyword-based search engines. It traces the evolution from keyword matching and algorithms like TF-IDF and PageRank to modern AI-powered search that uses natural language understanding (NLU) and vector-based retrieval methods. AI search leverages large language models (LLMs) like BERT, MUM, and more recent models to process queries contextually and semantically, generating direct, natural language answers rather than just lists of links. The stages of AI search include query understanding, semantic retrieval via embeddings, answer generation using retrieval augmented generation (RAG), and user feedback. The video also discusses the impact of AI search on SEO, noting that existing SEO practices still matter but must adapt to focus more on human-like, conversational content and clear, crawlable web structures. Experts highlight the importance of experience, expertise, authority, and trust (EEAT) for AI evaluation, and caution that JavaScript-heavy sites may pose challenges for AI crawling. Overall, AI search is reshaping information discovery, consumption, and the organization of content on the web.