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Not OP but I've been playing with similar technology as a hobby. >1. Are the sources used by the AI's learning phase trustworthy? (e.g. When will models be sophisticated enough to be trained to avoid some potentially problematic solutions?) Probably not, but for most domains reviewing the code should be faster than writing it. >2. How would an AI-generated solution be maintained over time? I would imagine you don't save the original prompts. Rather, when you want to make changes you just give the AI the current project and a list of changes to make. Copilot can do this to some extent already. You'd have to do some creative prompting to get around context size limitations, maybe giving it a skeleton of the entire project and then giving actual code only on demand. > When can my company host a viable trained model in a proprietary environment? Hopefully soon. Finetuned LLaMA would not be f…

Thanks for the questions. I’ll reply just a bit later. I’m in a train and reading/writing makes my stomach sick

> Given a data model and information about the data (relationships, intents, groupings, etc.) output a fully deployable service. From unit tests through container definitions, and everything I can think of in-between (docs, OpenAPI spec, log forwarder, etc.) AWS roughly has one of these in Amplify. The data mapping parts are pretty great, though lots of the rest of it suck. The question I'd ask is if those parts suck by nature of the setup, or is it just amplify that has weird assumptions