That way the tiny model isn't acting as a potentially incorrect interpreter but you still get semantic search to help you find the right documents quickly.
I suppose there is something to be said for a good summarization, I just worry that these tiny models aren't really GOOD summarizers.
I'm all up for using the better/simpler task for the job. That's been a thing lately also with the Jev/decision models hype, most of those things can either already be done by a pretrained classifier[0], or you can start with a bigger model and then train your smaller/adhoc one when you have some data (it doesn't even take much for most applications, you can get about 95% accuracy on email classification with 50-100 examples)
0: http://jeffyclassify.com/blog/when-llms-fail-at-classificati...
The bigger issue in this context would be canning, such as incorrectly explaining the mechanisms to avoid botulism (acid and/or pressure canning), or perhaps incorrectly wiring in repairs. However, again, it’s best to practice with these prior to emergencies anyway.
It should be noted though, that the model does source its claims, so you can fact check it and should review this information regularly.