You've built a prototype. It works in demos. Stakeholders are excited. Now comes the hard part: turning it into something that works reliably, at scale, in the real world. This playbook maps the journey.
The prototype trap
Prototypes are designed to show what's possible. Production systems are designed to work reliably. These are fundamentally different goals. The code that impressed in a demo will fail in production. Plan for a significant rewrite, not a polish.
A prototype proves you can do it once. Production proves you can do it a million times.
The production checklist
- Error handling for every failure mode
- Logging and monitoring for debugging
- Rate limiting and cost controls
- Input validation and output filtering
- Fallback behavior when AI fails
- Performance optimization for latency
- Security review and access controls
- Documentation for maintenance
Evaluation infrastructure
You need to know when your AI is working and when it isn't. Build evaluation infrastructure from day one: test datasets, automated quality checks, human review workflows, and dashboards that surface problems before users report them.
The cost of scale
AI costs scale with usage. That $20/month prototype becomes $20,000/month in production. Model this carefully. Optimize aggressively. Consider caching, smaller models for simple queries, and batch processing where latency allows.
Iterative hardening
You can't anticipate every production issue in advance. Plan for an iterative hardening period after launch: dedicated time to fix issues, optimize performance, and improve based on real-world usage. This isn't technical debt; it's the natural maturation of AI systems.
The timeline reality
If your prototype took two weeks, production will take two to three months minimum. If it took a month, plan for four to six months. This isn't pessimism; it's the consistent pattern we've seen across a decade of projects. Plan accordingly.