How To Verify Ai Output

Everyone agrees AI makes you faster but nobody agrees on how to verify the output.

This is not a new problem since SREs have been solving the "how do I trust this system without inspecting every single thing it does" problem for years. We stopped checking every server manually a long time ago.

Instead we built observability layers, defined SLOs, set up automated validation, and created feedback loops that tell us when reality diverges from expectation. The trust is instrumented, not blind or manual.

The engineers who are getting the most out of AI right now are doing the same thing. They're not reviewing every line the model generates word by word and they're not shipping it without looking. They're building a personal validation layer. This means they have a set of checks they run on AI output the same way they'd run checks on any system they don't fully control.

What does that look like in practice?

You know what the code is supposed to do before you ask for it. You read the output for logic not just syntax. You run it against edge cases you already have in your head from domain knowledge. And you notice when it solves a slightly different problem than the one you actually have.

That last one is the one that gets people. AI is very good at solving the problem it thinks you're asking. But the senior skill is knowing whether that's the problem you actually have.

#SRE #DevOps #SoftwareEngineering #AIAssistedDevelopment #EngineeringLeadership