The Prototype Trap
Every AI demo looks amazing. Every production system is a nightmare. The gap between them is real and predictable.
What Changes at Scale
Reliability
- Prototype: Works 80% of the time, you manually retry
- Production: Needs 99.5%+ success rate with automated fallbacks
Latency
- Prototype: 5-10 second responses feel fine
- Production: Users expect <2 seconds
Cost
- Prototype: $0.50/test doesn't matter
- Production: $0.01/request × 1M requests = $10K/month
Observability
- Prototype: Print statements
- Production: Logging, tracing, alerting, dashboards
The Playbook
Phase 1: Hardening
- Add input validation and sanitization
- Implement retry logic with exponential backoff
- Add fallback models (e.g., GPT-4o → GPT-4o-mini)
- Set timeout limits
Phase 2: Evaluation
- Build a test set of 100+ representative inputs
- Define evaluation metrics (accuracy, relevance, format compliance)
- Automated regression testing
Phase 3: Infrastructure
- Request queuing (don't hammer the API)
- Caching for repeated queries
- Rate limiting per user
- Database for conversation history
Phase 4: Monitoring
- Log every request/response pair
- Track latency percentiles (p50, p95, p99)
- Alert on error rate spikes
- Cost tracking per feature/user
The Hard Truth
Shipping an AI prototype takes days. Making it production-ready takes months. Plan accordingly.