Classical search is deterministic. Crawlers fetch pages, an inverted index stores the strings on them, and a ranking system matches a query’s keywords against that index to assign each document a position on a static results page. Run the same query twice and you get the same ten blue links. That stability is what made rank tracking meaningful.

Generative search is probabilistic. A large language model (LLM) doesn’t look up a stored answer. It retrieves candidate passages by vector similarity, often across several retrieval hops (multi-hop RAG), and then generates a response token by token, each one sampled from a probability distribution. It retrieves a large pool of content, discards most of it, and builds the answer from the small fraction of sources it feels confident using.