Ok,
I have heard a lot of good, and bad comments, so I want to clear some things up.
First of all, we are using an OpenAI based back-end, we are in the process of developing our own LLM for the task of text fitting.
Secondly, the LLM in our approach is currently only used for text translation. This meaning, we assume the LLM dosnt know anything, and thus before the users input even reaches the LLM it has to hit a couple other models which determine if the statement is a fact or not, then an intermediary text analyses model to extract understandable queries from the text which can then be used to search information about the topic from a DB (for example, Google FC API, or a custom dataset of documents known good).
After that process, all the data is presented to an LLM which can then fit the known good data into the context of the users input.
The LLM itself is never trusted with data.
Of course for a system like this to work, we would need access to a DB and those intermediary models, which as you can guess, will take a while to build and develop.
For now, we are pushing our beta without this system but a dumbed down non-optimized (and definitely flawed) version to test scalability and fix bugs, test security, and check scalability for our back-end platform.
In case anyone was wondering anything about my credentials, I am one of the lead back-end developers working on the project. I am also free to answer any reasonable questions anyone might have about Factful.