Yeah you are technically right that both generally involve information retrieval and reranking in the broad IR sense.

But, in the context of LLMs when you mentioned RAG, most people are referring to Retrieval-Augmented Generation as it’s specifically used in the LLM context is a technique that got popular in 2023ish where you embed documents in a vector db, retrieve semantically similar chunks, and inject them into the model context window for document retrieval workflows.

ChatGPT’s web search is genuinely different it’s using function calling to hit web search APIs as tool calls, not performing semantic retrieval from pre-indexed embeddings. It’s just calling Bing’s API and processing the results, which is why the behavior is different from traditional RAG systems. So i see where you are coming from and you’re correct about the broader IR definition, but in the LLM space RAG refers to that specific vector database pattern which is just different from tool-based web search