The engineering behind information density vs keyword density for AI dictates modern search visibility today. Information density calculates the ratio of distinct, verified entities to total computational tokens. Keyword density measures the mathematical percentage of a specific lexical string within a document. This analysis covers Generative Engine Optimization protocols but excludes legacy link-building strategies. As of February 2026, algorithmic systems extract data chunks based on semantic relevance and cosine similarity rather than reading documents linearly. Webmasters must adapt immediately. For more information, read this article: https://www.linkedin.com/pulse/information-density-vs-keyword-generative-engine-ai-search-nicor-hgurc/ The Mechanics of Semantic Vector Retrieval Large Language Models evaluate text through high-dimensional vector embeddings, treating conversational filler as computational waste. AI companies, such as Anthropic, face immense processing power cost...
Retrieval-Augmented Generation (RAG) is the specific framework that allows Large Language Models (LLMs) to fetch external data before writing an answer. In my SEO consulting work, I define it as the bridge between a static AI model and a dynamic search index. This technology powers Google's AI Overviews and stops the model from hallucinating by grounding it in real facts. Unlike standard keyword-based crawling, retrieval in this context specifically refers to neural vector retrieval , which matches the semantic meaning of a query to a database of facts rather than simply matching text strings. The process works by replacing simple keyword matching with Vector Search . When a user asks a complex question, the system does not just look for matching words. It scans a Vector Database to find conceptually related text chunks. The Retriever acts like a research assistant that pulls specific paragraphs from trusted sites and feeds them into the Generator. This means your content must be...