How LLM Selection Rate Optimisation Works (James Dooley Interviews Charles Floate)
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Show Notes
What is selection rate optimisation in AI search and why does it matter for SEO in large language models? In this discussion, James Dooley and Charles Flo break down how selection rate optimisation (SRO) works within LLMs such as ChatGPT, Gemini and Perplexity. The conversation explains how AI systems run multiple grounded searches, gather hundreds of potential sources and then select a limited number of trusted documents to build an answer. Charles explains why only a small set of sources are chosen and how content structure, semantic optimisation and authority influence whether a page is selected. They also discuss how content chunking, headings, entity signals and search rankings influence extraction by AI models. The episode also explores the importance of third party corroboration, entity authority, brand reputation signals and positive sentiment across the web to strengthen AI visibility and improve selection rate optimisation in AI search.