Hey there, he’s referring to generative ai. That is, using ai to create text (or images, or other media) whereas currently your use case is as a chat bot (e.g. tell me about xyz).
The generative ai side of things is used a lot in a lot of apps: jasper, surfer, frase, but also some of your direct competitors like coda and notion. And also getting more and more built into Google Workspace and Microsoft Office. Think of it like this - instead of “hey, how do I create a formula to automatically add X +Y,” and instead think “help me write the page of our user guide that deals with X + Y,” and even moreso - particularly able to improve over time - “use my site’s history and reference xxx codebase and write a great article that instruct one how to deal with X + Y” and even more - “automatically write the outline, incorporate 45 LSI phrases, reference six sites from authoritative sources, add the 12 most asked questions and provide the answers, and incorporate EEAT so that - even though content marketing is bad and doesn’t work (hehe I read your blog) - it will work in this case.”
This of course can be expanded into emails, social media posts, Q&A, etc. - lots of companies are doing that - but - considering your model - providing that out-of-the-box is a natural fit and would help a lot of startups - particularly any that may try to use Fibery as a CMS. Also, we are approaching a point (if not there already) where it’s less about a differentiator and more about a perceived shortcoming if you don’t have it - particularly given how aggressive MS Office and Google Workspace are going.
In terms of some of the other comments I see, they make sense and you should look to incorporate them into things early-on. Some of them, plus some other things:
(1) Don’t limit yourself to openai. Maybe at the start, but there are others and the open source LLMs coming out of Meta are particularly interesting. So, too, for programming and development, are some of the models out of hugging face and also particularly Phind.
(2) Keep in mind there is a lot of open source stuff that is amazing right now, particularly stability.
(3) Another post mentioned space-specific fine-tuning. I agree with this. But first I will say that you should pass along user-enterable custom instructions. These need not be complex or complicated but can take out-of-the-box openai from a 5 to an 8+ in about 10 minutes (it’s as simple as two text fields). What’s more, if done right, they need not be a one-time-thing but instead could be per-space, per-database, per-personna, per-need, etc.
(4) From there, you can enable fine-tuning but that’s a bit more effort; however, there is an innovative way to do this that is straightforward - though I’ll have to write a full post on it - and could use some feedback about your tech stack first. Fine-Tuning enables you to “train” in a much more specific way than merely custom instructions. You can train data, differentiation, tone, style, etc. So your customer space could have one particular voice while your marketing space could have another and your product development could have yet another. Once you’ve evolved, you come to appreciate just how powerful (and surprisingly simple) fine-tuning is.
Feel free to reach out regarding this, as I have used Fibery I’ve naturally been considering how and where AI could (and should be integrated).