The approach in the paper has rough edges, but the metrics are bonkers (double digit percentage POINTS improvement over dual encoders). This paper was written before the LLM craze, and I am not aware of any further developments in that area. I think that this area might be ripe for some break through innovation.
But if all you wanted is a search engine that's a bit easier.
The problem is often that a huge wiki installation etc will have a lot of outdated data etc. Which will still be an issue for an llm. And if you had fixed the data you might as well just search for the things you need no?
The best at least for now is to just use OpenAI’s custom gpt and with some clever (but not hard) it’s quite good.
Most companies lack the expertise and resources. Kapa means they get a docs bot while maintaining focus on what they do best.
Kapa must be doing something right since they seem to be growing. Having used it in a few discords, it's what I'd expect for quality for a saas product built on current ai capabilities.
It's marketing. The person you responded to said they're all marketing. Saying they "must be doing something right" because other people are also falling for it is how you get scammed.
Even OpenAI, GP's alternative, is listed as using Kapa... and no public sign ups available yet either
I saw a glimpse of the internal dashboard companies get. It's much more than just question answering. Another big piece is the feedback, seeing user interaction, and being able to improve things over time
But, the holy grail is an LLM that can successfully work on a large corpus of documents and data like slack history, huge wiki installations and answer useful questions with proper references.
I tried a few, but they don’t really hit the mark. We need the usability of a simple search engine UI with private data sources.