How do I know an AI system is reliable enough to ship?
You can build the system. It runs. It answers. It does what you designed it to do. Then you ask if it's reliable enough. Or cheap enough to run at scale. And you freeze. You reread the same benchmarks. You ask other people what they would do. Nothing resolves it. This is not a gap in your knowledge. You already have that. This is a gap in your reps. You have not yet made a hundred small calls on the reliability-versus-cost line and lived with what happened after. Judgment is built by deciding, not by learning more. So the work here is not another paper on evaluation metrics. It is choosing, this week, on one real system, what 'good enough' means. Then shipping it before you feel ready.
You will not find this answer by studying more. You already know how to build the thing. What you lack is judgment, and judgment comes only from deciding — under real constraints, again and again. Stop hunting for the perfect threshold. Ship something small, watch what breaks, adjust. That is the method.
What changes unlock by starting
- A working definition of 'reliable enough' you can reuse on your next system, not just this one.
- A cost ceiling you set on purpose, instead of one you discover in a bill.
- A shipped system with real data on what breaks — worth more than another benchmark.
- Less time circling the decision. More time making it.