Looks cool, I like it, but given AIs propensity to outright lie to us (hallucinate), what steps are being taken to verify the facts of the summaries it is giving?
It's quite unlikely to hallucinate in this because it's not asked to answer a question from information it was trained on; the facts in the summaries are fed in the context of the request.
That's not to say it will always get everything right of course; in my experimentation with LLM-powered summarization of news articles, I find the thing it would more often struggle with is quote attribution. The way some writers formulate who said what in a conversation sometimes confused the models I use (mostly Mixtral these days, which is about GPT 3.5 level), and it would claim that someone said something that I knew this person definitely did not say, and I would check the actual article and it turned out the journalist said that thing and the interviewee said the opposite, but the LLM thought it was the interviewee who said it.
@guizzy has it right. Much more unlikely to hallucinate because all of the information it needs is submitted in the API request -- its actually explicitly asked to not come up with anything new.
Of course though this is a concern that needs to be closely watched as AI summaries become more common in the world.
That's not to say it will always get everything right of course; in my experimentation with LLM-powered summarization of news articles, I find the thing it would more often struggle with is quote attribution. The way some writers formulate who said what in a conversation sometimes confused the models I use (mostly Mixtral these days, which is about GPT 3.5 level), and it would claim that someone said something that I knew this person definitely did not say, and I would check the actual article and it turned out the journalist said that thing and the interviewee said the opposite, but the LLM thought it was the interviewee who said it.