everyone's focused on prompting, tool selection, workflow automation. all important. but the single biggest gap I keep seeing between people who are good at AI and people who are great at it is output verification. most people's process: generate something with AI, skim it, ship it. that's not an AI skill problem, it's a quality control problem. we ran data on this at aisa.to — out of 412 assessments, the average AI skills score was 52/100. Safety and critical thinking scored lowest (45/100). the pattern: people are decent at getting AI to produce things, terrible at systematically checking whether those things are correct. the fix isn't complicated. before shipping any AI output: check the logic structure, spot-check 2-3 factual claims, and ask the model to argue against its own answer. takes 2 minutes, saves you from the 1-in-5 time the output is subtly wrong. anyone else building verification into their AI workflows? what does your process look like?