staff level

Google's FDE loop added an AI-first round, and this is the opener. The skill being graded is not prompting, it is decomposition: turning a fuzzy sentence into scoped units with named interfaces an assistant can one-shot. Get the seams wrong and no prompt saves you.

The naive fix is to nag the model in every prompt. The staff move is to treat your constraints as a versioned policy library injected as system context, so compliance is structural, then verify it with a linter because prompts are advisory, not enforced.

When the model writes the code, your job shifts to specifying and checking it. The strong answer is a multi-layer validation pipeline that tests behaviors and contracts, not strings, plus the uncomfortable reframe that testing is now the bottleneck skill, not typing.

You can't read the model's weights, so you debug the inputs you control. There's a four-bucket triage, prompt, context, model, or spec, that localizes the fault fast, and the real deliverable is feeding each diagnosis back so the same failure can't recur.

AI code fails differently from human code: it's confidently plausible. The dangerous defects are the ones that pass a quick read and a happy-path test, missing authorization, hallucinated APIs, hardcoded secrets, unsafe deserialization. Here's the checklist and the automated catch for each.

If you read every AI PR line by line, you become the throttle the assistant was meant to remove. The staff answer is a risk-tiered trust model: route by blast radius, let gates clear the low-risk volume, and spend human attention only where reversibility is low.