The hardest part of AI development isn't the 'AI' — it's the 'Development'. Shipping a prototype that works on your machine is easy. Shipping a production service that handles 10,000 users with consistent latency and cost is where the real work begins.
The 90/10 Rule
90% of your time will be spent on the data pipe, the error handling, and the monitoring. The remaining 10% is the actual model call. I've learned that successful AI products are built on top of traditional, rock-solid engineering practices. You can't skip the fundamentals just because you're using a fancy model.
MLOps vs DevOps
Monitoring an AI app requires a new set of metrics. It's not just 500 errors; it's 'drift', 'vibe-checks', and 'cost-per-token'. Scaling products like OFFPITCH taught me that observability is your only defense against a model that silently decides to stop following your instructions.