Isn’t it basically a traditional search (either keyword based, vector based -embeddings have been around for years-, or a combination of both) where you take the top N results (usually not even full docs, but chunks due to context length limitations) and pass them to an LLM to regurgitate a response (hopefully without hallucinations), instead of simply listing the results right away? I think some implementations also ask the LLM to rewrite the user query to “capture the user intent”.
Isn’t it basically a traditional search (either keyword based, vector based -embeddings have been around for years-, or a combination of both) where you take the top N results (usually not even full docs, but chunks due to context length limitations) and pass them to an LLM to regurgitate a response (hopefully without hallucinations), instead of simply listing the results right away? I think some implementations also ask the LLM to rewrite the user query to “capture the user intent”.
What I’m missing here? What makes it so useful?