Every RankingHacks post tagged AI Search — strategy, tactics, and takeaways for solo publishers and affiliate marketers, newest first.
6 posts
Google Cloud just published the Open Knowledge Format — a way to store knowledge as a folder of linked markdown files for AI agents. The SEO crowd is already calling it the next llms.txt. It isn't, and the gap between what OKF actually is and what it's being sold as is the useful part for a solo publisher.
For three weeks this site ran its own GEO playbook on itself — reframed legacy posts, shipped a citation pillar, wired clean schema, and tracked the numbers. Here's the unflattering truth: the leading work shipped, the lagging metrics haven't moved, and the one durable AI citation is exactly the niche the diagnosis predicted. The baseline, in public.
Schema doesn't rank you in an LLM — that part is snake oil. What it actually does is disambiguate your entities and hand the retriever clean structure to parse. This is the practical setup: which schema types earn their keep, why a connected @graph beats scattered JSON-LD islands, and the exact wiring this site runs.
Answer engines don't trust pages — they trust entities. Before ChatGPT or Perplexity names you, it has to know who you are and decide you're reliable. This is how that resolution works, the four rungs from mentioned to cited, and the signals that move you up them.
The definition of GEO is settled; the open question is operational — what do you actually change on a page to get an AI answer to quote it? This is the playbook: how ChatGPT and Perplexity each pick their sources, what differs between them, and the seven moves that move a passage from retrieved to cited.
GEO — generative engine optimization, also called answer-engine optimization (AEO) — is the practice of getting your pages cited inside AI answers, not just ranked on a results page. Here's the working definition, how answer engines actually pick their sources, and what a solo publisher should do about it.
