LLMs are becoming a discovery layer. Users ask a question, the model synthesizes an answer, and then it may cite a few sources. That shifts the goal from “rank and win a click” to “be the most useful, extractable, verifiable source in the retrieval set.”
For TalentHacked.com (UK Global Talent Visa platform), this is a solvable engineering problem: ship content that a headless retriever can fetch, chunk, embed, and cite.