| I do! I posted this a while down below but I guess the way the algorithm here works it got super deprioritized. Full repost: I can chip in from my tech consulting job where we ship a few GenAI projects to several AWS clients via Amazon Bedrock. I'm senior level but most people here are pretty much insulated. I think whoever commented once here about more complex problems being tackled, (and the nature of these problems becoming broader) is right on the money. Newer patterns around LLM-based applications are emerging and having seen them first hand, they seem like a slightly different paradigm shift in programming. But they are still, at heart, programming questions. A practical example: company sees GenAI chatbot, wants one of their own, based on their in-house knowledge base. Right then and there there is a whole slew of new business needs with necessary human input to make it work that ensues. - Is training your own LLM needed? See a Data Engineer/Data engineering team. - If going with a ready-made solution, which LLM to use instead? Engineer. Any level. - Infrastructure around the LLM of choice. Get DevOps folk in here. Cost assessment is real and LLMs are pricey. You have to be on top of your game to estimate stuff here. - Guard rails, output validation. Engineers. - Hooking up to whatever app front-end the company has. Engineers come to the rescue again. All these have valid needs for engineers, architects/staff/senior what have you — programmers. At the end of the day, these problems devolve into the same ol' https://programming-motherfucker.com And I'm OK with that so far. |