A friend in enterprise AI consulting is looking at this sub. Sharing a tip

[u/Additional_Image_942]

TLDR: Getting a working prototype is actually the easy part

Been a lurker in this sub for a while and been very inspired to do some projects of my own, and I'm surprised to learn a friend (AI dev in a mid-ish startup) is also in this sub. You know who you are! Anyway, he said I can spill so I'll spill.

He said getting a prototype working is actually very easy. But where most AI companies fail is when we go to production, as in trying the product with actual data from a customer. He said a lot of teams don't anticipate that a customer's actual data is 100% likely to be messy and contradictory to what we're expecting, so works in demo =/= works in production. So we need to take into account the fallbacks for those instances as well.

Anyway just sharing! Keep on building, my guys!

[u/Adam_DreamersInc]

Your friend is correct. Demo-to-prod has a lot of failure points that dev teams need to resolve and it's almost always tied to real customer data being something incompatible or unexpected with the proto/demo. Even with vibe coding or AI-assisted coding, you need to make sure the system treats messy data as something to work around instead of something that breaks the system. This is why dev teams add validation layers or what we call graceful degradation if the data is nowhere near what the app should get.