Your LLM prototype works great in the demo. Users love it. Leadership is excited. Then you try to deploy it, and everything breaks.
Sound familiar?
The Gap Between Demo and Production
Prototypes optimize for speed and wow factor. Production systems optimize for reliability, cost, and safety. Bridging that gap requires discipline.
The Checklist
1. Prompt Engineering & Testing
- [ ] Prompt versioning in place
- [ ] Evaluation set with 100+ examples
- [ ] Automated regression tests
- [ ] A/B testing framework ready
2. Safety & Content Filtering
- [ ] Input validation and sanitization
- [ ] Output content moderation
- [ ] PII detection and redaction
- [ ] Jailbreak attempt detection
3. Performance & Cost
- [ ] Response time p95 < 2 seconds
- [ ] Cost per query tracked
- [ ] Caching strategy implemented
- [ ] Fallback to smaller/faster model for simple queries
4. Monitoring & Observability
- [ ] Quality metrics dashboards
- [ ] Cost tracking per user/feature
- [ ] Latency monitoring
- [ ] Error rate alerts
5. Data & Context Management
- [ ] Vector DB indexed and optimized
- [ ] RAG retrieval quality measured
- [ ] Context window management
- [ ] Data freshness tracking
6. User Experience
- [ ] Streaming responses implemented
- [ ] Graceful error messages
- [ ] Human escalation path
- [ ] Feedback collection mechanism
Common Pitfalls
Pitfall #1: No Evaluation Framework You can't improve what you don't measure. Build evaluation sets early.
Pitfall #2: Ignoring Costs That $0.02 per query adds up fast at scale. Monitor from day one.
Pitfall #3: No Failure Handling APIs fail. Models hallucinate. Plan for it.
Pitfall #4: No Version Control Prompt changes can break everything. Version prompts like code.
Real Numbers
Here's what we see in production LLM apps:
- Response time: 1.5-4 seconds (depending on complexity)
- Cost: $0.01-$0.15 per query
- Error rate: 2-5%
- User satisfaction: 70-85% (with human escalation)
Your mileage will vary, but these are realistic targets.
Start Small, Iterate Fast
Don't try to build the perfect system on day one. Ship a limited beta, measure everything, and iterate. The fastest path to a great production system is through a mediocre v1.
Need help productionizing your LLM application? Let's talk.