AI in Production: 5 Lessons From the Trenches
Shipping AI features is easy. Keeping them reliable, fair, and cost-effective in production? That's the hard part.
Over the past two years, we've shipped AI features into 15+ production applications. Here are the lessons that no tutorial teaches you.
1. Latency Matters More Than Accuracy
A model that's 95% accurate in 200ms will always beat a model that's 99% accurate in 3 seconds. Users don't wait.
2. Prompt Engineering Is Software Engineering
Treat prompts like code: version them, test them, review them, monitor them. We use structured output schemas and automated regression tests for every prompt.
3. Start With Rules, Add ML Later
Most "AI features" don't need machine learning. Start with deterministic rules, measure where they fail, then add ML to handle the edge cases.
4. Cost Will Surprise You
API costs for LLMs scale non-linearly with usage. We've seen bills jump from $500/month to $50,000/month. Cache aggressively and batch when possible.
5. Monitoring Is Not Optional
Model drift, data quality issues, and adversarial inputs will break your AI features in production. Invest in monitoring from day one.