Two years building production LLM systems taught me that research prototypes ≠ production systems.
The Reality Check
Our customer vulnerability prediction system started with GPT-4 for everything. Great demos, terrible production:
- Too slow (5+ seconds)
- Too expensive (£100+ per 1000 requests)
- Unreliable at scale
What Actually Works
1. Hybrid Architecture
Input → Fast Classifier → [High Confidence] → Response
↓
[Uncertain] → LLM → Human Review → Response
Result: 80% handled by fast models, 10x cost reduction.
2. Treat Prompts as Code
- Version control everything
- A/B test variations systematically
- Monitor performance continuously
- Build evaluation frameworks
3. Cost Engineering
- Cache common responses
- Use cheaper models for simple tasks
- Batch requests where possible
- Set strict token limits
4. Human-in-the-Loop
Even 92% accuracy needs human oversight for critical decisions. Flag uncertain cases, learn from edge cases.
Key Metrics
- Latency: <1s for 90% of requests
- Cost: 10x reduction vs naive approach
- Accuracy: 92% with human backup
- Uptime: 99.9% reliability
Takeaways
- Engineer for efficiency from day one
- Measure everything you can’t optimize blindly
- Stay pragmatic - solve the business problem
- Plan for scale - costs compound quickly
Building production LLM systems is challenging but rewarding. Balance technical excellence with practical constraints.