AI Engineer’s Field Guide: A practical playbook for designing production AI systems

I built this because I was intimidated. I'd spent years on model training and writing production-grade code, but never actually deployed an AI system. And the gap felt huge. Making an API call and getting a response is nowhere close to a real application. When I went looking for help, resources were scattered, and a lot of LinkedIn content just showed off "here's a problem, here's my solution" without ever sharing the underlying framework you could reuse for any problem. So I built the framework I wished existed: a top-down method, a 5-pillar architecture model, decision trees for the forks I kept hitting, and an incident playbook for the failures I actually ran into. If you've felt that same intimidation moving from "I can train a model" to "I can build a system" then this is for you. Would love your feedback.