AI in Production
The gap between demos and production AI systems is wider than most teams expect.
Everyone has a working demo. Few companies have AI in production that actually solves real problems.
The gap between these two isn’t technical sophistication - it’s understanding production requirements:
Latency budgets are real. Your LLM-powered feature can’t take 30 seconds to respond. Users won’t wait. You need streaming, caching, and fallbacks. The demo didn’t need any of this.
Cost per inference matters. That $0.02 per API call adds up fast when you’re processing millions of requests. Suddenly you’re spending $40K/month on what was supposed to be a feature enhancement.
Hallucinations aren’t acceptable in production. Your demo could get creative. Your production system needs guardrails, validation, and human-in-the-loop workflows for anything that matters.
Compliance isn’t optional. If you’re in healthcare or finance, every AI output needs to be explainable and auditable. That changes your architecture significantly.
[Placeholder for specific examples of production AI challenges, solutions, and trade-offs]