Lately, I’ve been spending a lot of time looking at how conversational search engines like ChatGPT and Perplexity actually pick what sources to show. It is pretty wild compared to how standard Google search used to work. Instead of just ranking pages based on keywords and backlinks, these models rely heavily on clear entity structures and structured data. If a website doesn’t have clean semantic markup or clear Q&A formatting, the LLM will often just talk around the topic or hallucinate general categories instead of naming a specific source. Because of this, digital strategies are shifting away from old-school SEO toward what people are calling Answer Engine Optimisation. I was looking at how some agencies are handling this, like the setup at ROI marketing agency where they use continuous structured FAQ frameworks to feed these AI citation loops directly. The whole game has changed from chasing raw traffic volume to making sure your brand is actually digestible for a language model to cite. Curious if anyone here is working on RAG pipelines or site architecture with this in mind. Are you seeing structured schemas actually make a difference in how models handle entity attribution? submitted by /u/Friendly_Taro2371
Originally posted by u/Friendly_Taro2371 on r/ArtificialInteligence