alt Hacker NewsAlso anything that doesn't look like a SaaS app does very badly. We had an internal trial at embedded firmware and concluded the results were unsalvageably bad. It doesn't help that the embedded environment is very unfriendly to standard testing techniques, as well.
You will need to build an accessible knowledge base for the topics for which the models have not had extensive training.
Proprietary embedded system documentation is not exactly ubiquitous. You must provide reference material and guardrails where the training is weakest.
This applies to everything in ML: it will be weakest at the edges.