AI, Machine Learning, and the Future of Search
Positioning your brand for generative search, AI overviews, and LLM ecosystems.
The search landscape is fragmenting. Traditional organic results now compete with AI Overviews, ChatGPT citations, Perplexity answers, and countless other AI-powered interfaces. These articles help you understand how these systems work—and what you can realistically do to appear in them.
Articles in this section cover the technical mechanisms behind RAG (Retrieval-Augmented Generation), how AI crawlers differ from traditional search bots, and practical strategies for earning citations in generative responses.
All articles
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Semantic Relevance: How Search and AI Systems Evaluate Meaning
How semantic relevance works in traditional search and AI retrieval systems, from knowledge graphs and embeddings to practical implications for content structure.
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Earning AI Citations: Content Strategy for Generative Search
How to actively optimise for AI visibility through content structure, off-site citation management, and brand positioning in generative search surfaces.
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AI Crawlers and Access Control: Managing Bot Access for Training, Retrieval, and Search
How to manage access for AI training versus retrieval crawlers, from robots.txt to edge-layer enforcement and licensing signals.
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How LLMs and RAG Systems Retrieve, Rank, and Cite Content
How RAG systems retrieve, rank, and cite content—from vector embeddings and re-ranking to information gain and citation attribution.
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Understanding AI Visibility: Fundamentals, Measurement Limits, and Risks
What AI visibility means, why tracking it is difficult, and what risks brands face in generative search—a foundational guide before diving into tactics.
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