What I Learned Building AI Features Into Real Products
Practical lessons from integrating LLMs into production systems. The gap between demo and deployment is wider than you think.
Everyone has a ChatGPT wrapper. Very few people have AI features that actually work reliably in production.
I've spent the last year integrating AI — primarily OpenAI's models — into products that real users depend on.
The Demo Trap
It takes about 30 minutes to build an impressive AI demo. It takes weeks to make that same feature production-ready. The gap is enormous.
Prompt Engineering Is Software Engineering
Prompts aren't magic incantations. They're code. They need to be version-controlled, tested, and iterated on.
The Latency Problem
LLM API calls are slow compared to everything else in your stack. A database query takes milliseconds. An AI completion takes seconds.
Output Validation Is Non-Negotiable
LLMs are probabilistic. They will return something unexpected. Your system needs to handle this.
Cost Management
AI API costs scale linearly with usage. Cache responses when possible. Use smaller models for simple tasks. Reserve expensive models for complex reasoning.
The Real Takeaway
AI is a powerful tool, but it's still a tool. It needs the same engineering discipline as any other dependency — monitoring, error handling, testing, and cost awareness.