SEO Expert Urges Marketers to Query Raw LLM APIs to Expose Retrieval Gaps

Leeds-based SEO consultant Martin McGarry, with two decades of industry experience, recommends a simple terminal experiment to understand how AI chat products differ from raw language models. By querying a bare LLM API directly and comparing the response to the same question in ChatGPT or Gemini, users can observe how retrieval-augmented generation (RAG) fills in the gaps the base model cannot. McGarry argues this demonstrates that SEO is not obsolete, since LLMs must still fetch external content to produce useful answers, meaning website visibility in those retrieval sources still matters. He suggests that rather than focusing solely on llms.txt or markdown files, practitioners may benefit from improving machine-readable outputs like RSS feeds and JSON APIs, which are likely surfaces for server-side fetches. McGarry acknowledges this is a hunch, but believes optimizing existing feed infrastructure could be an overlooked opportunity in the emerging landscape of AI-driven search.