>2. How much value is added by feeding the search results through the LLM with the user's prompt, vs just returning the results directly to the user? These sys

>2. How much value is added by feeding the search results through the LLM with the user's prompt, vs just returning the results directly to the user? These systems usually retrieve the "closest N" snippets. Which might include irrelevant ones if there aren't enough close matches. The LLM will favor the relevant info when composing an answer. >3. Are there good techniques being developed for handling citations in the LLM output? IIRC Google had this in Bard Like sharemywin said, you can include citation info with the snippets when making the prompt. In a proof-of-concept I did at work, I was having trouble forcing the gpt-3.5-turbo model to include URL citations. What eventually worked was adding an extra snippet to the list of relevant ones: a few sentences from the Wikipedia page for Lorem Ipsum plus its URL (arbitrary choice, just had to not overlap with any topic from my documents). Then the prompt included an example question about Lorem Ipsum, with an example answer citing the URL.