I can’t speak for the original poster, but I also use Kagi and I sometimes use the AI assistant, mostly just for quick simple questions to save time when I know most articles on it are gonna have a lot of filler, but it’s been reliable for other more complex questions too. (I just would rather not rely on it too heavily since I know the cognitive debt effects of LLMs are quite real.)
It’s almost always quite accurate. Kagi’s search indexing is miles ahead of any other search I’ve tried in the past (Google, Bing, DuckDuckGo, Ecosia, StartPage, Qwant, SearXNG) so the AI naturally pulls better sources than the others as a result of the underlying index. There’s a reason I pay Kagi 10 bucks a month for search results I could otherwise get on DuckDuckGo. It’s just that good.
I will say though, on more complex questions with regard to like, very specific topics, such as a particular random programming library, specific statistics you’d only find from a government PDF somewhere with an obscure name, etc, it does tend to get it wrong. In my experience, it actually doesn’t hallucinate, as in if you check the sources there will be the information there… just not actually answering that question. (e.g. if you ask it about a stat and it pulls up reddit, but the stat is actually very obscure, it might accidentally pull a number from a comment about something entirely different than the stat you were looking for)
In my experience, DuckDuckGo’s assistant was extremely likely to do this, even on more well-known topics, at a much higher frequency. Same with Google’s Gemini summaries.
To be fair though, I think if you really, really use LLMs sparingly and with intention and an understanding of how relatively well known the topic is you’re searching for, you can avoid most hallucinations.
I can’t speak for the original poster, but I also use Kagi and I sometimes use the AI assistant, mostly just for quick simple questions to save time when I know most articles on it are gonna have a lot of filler, but it’s been reliable for other more complex questions too. (I just would rather not rely on it too heavily since I know the cognitive debt effects of LLMs are quite real.)
It’s almost always quite accurate. Kagi’s search indexing is miles ahead of any other search I’ve tried in the past (Google, Bing, DuckDuckGo, Ecosia, StartPage, Qwant, SearXNG) so the AI naturally pulls better sources than the others as a result of the underlying index. There’s a reason I pay Kagi 10 bucks a month for search results I could otherwise get on DuckDuckGo. It’s just that good.
I will say though, on more complex questions with regard to like, very specific topics, such as a particular random programming library, specific statistics you’d only find from a government PDF somewhere with an obscure name, etc, it does tend to get it wrong. In my experience, it actually doesn’t hallucinate, as in if you check the sources there will be the information there… just not actually answering that question. (e.g. if you ask it about a stat and it pulls up reddit, but the stat is actually very obscure, it might accidentally pull a number from a comment about something entirely different than the stat you were looking for)
In my experience, DuckDuckGo’s assistant was extremely likely to do this, even on more well-known topics, at a much higher frequency. Same with Google’s Gemini summaries.
To be fair though, I think if you really, really use LLMs sparingly and with intention and an understanding of how relatively well known the topic is you’re searching for, you can avoid most hallucinations.