Instructions
Launching an AI product is not like launching a conventional app. AI systems can degrade silently — producing worse outputs over time without any error being thrown. They can generate unexpected costs if usage spikes. They can fail in ways that are hard to detect from logs alone. Planning for production failure modes before you launch is what separates teams that handle incidents gracefully from teams that discover problems because a user posted about them publicly.
The common mistake is conflating "it works in testing" with "it's ready for production." Testing in a controlled environment doesn't reveal all the ways real-world usage will stress your system. Real users have unexpected inputs. Concurrent usage creates latency. Model providers have outages. Costs can spiral faste...
