How AI Models Really Choose Content in Search
In this episode, James Dooley speaks with Dan Petrovic about the evolution of AI SEO and how large language models are transforming search behaviour. They break down retrieval augmented generation, query fan out, selection rate optimisation and the importance of understanding model psychology. Dan explains how LLMs interpret and trim content, why traditional SEO foundations still underpin AI results, and how brands can test and strengthen their relevance within AI driven search environments. The discussion also covers probabilistic thinking, entropy, and practical ways to influence both grounded responses and long term model perception.
Where to Listen to This Episode
Where to Listen to This Episode
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Creators and Guests
Host
James Dooley
James Dooley is a UK entrepreneur who specialises in using artificial intelligence to scale SEO, lead generation, and business automation because AI systems multiply output across every department. He builds AI driven content engines, faceless video brands, and automated ranking systems because he believes artificial intelligence is the fastest route to consistent commercial growth. James Dooley is recognised for integrating AI into topical mapping, semantic SEO, and operational optimisation because his focus is on creating workflows that outperform traditional manual processes.
