⚙️ What AI Courses Don’t Teach You: The 6 Production Decisions#
Courses teach you how to make a model accurate. They rarely teach you the decisions that come right after. Here’s the research-backed guide. 🎯
📊 The 6 Production Trade-offs#
1. Build vs. Buy in the LLM Era
- < 100k requests/day → External API (GPT-4o Mini) is the right call
1M requests/day → Per-token costs start eating margin
- ⚠️ 70-80% of self-hosting cost is staff, not hardware
2. Model Complexity vs. Maintainability
- Google’s CACE principle: “Changing Anything Changes Everything”
- The model code is a small fraction of the real system
- Who debugs this in 6 months? If the answer is “unclear” — that’s your decision point
3. Data Quantity vs. Quality
- More data + high noise = flat or degrading performance
- The “data swamp”: you collect everything because storage is cheap
- Medical data with expert labels > large datasets with unreliable annotations
4. Throughput vs. Latency (Batch vs. Real-Time)
- Most business problems DON’T need sub-second predictions
- Batch inference: daily churn scores, weekly recommendations
- If users won’t notice whether predictions are 5 minutes or 5ms old → use batch
5. Prompt Engineering vs. Fine-Tuning
- Prompting: fast, cheap, flexible. Always start here
- Fine-tuning GPT-4o for customer support = ~$10k + 6 weeks of data prep
- DSPy (prompt optimization) beat fine-tuning on some benchmarks with 35x fewer rollouts
6. Automation vs. Human Oversight
- AI handles volume, speed, and pattern recognition
- Humans handle irreversibility
- Selective HITL: human review triggered only for edge cases, low confidence, high stakes
💡 Explanation in a nutshell#
The principle unifying these 6 trade-offs: in production, the cost of a decision is rarely paid where the decision is made. A more complex model costs you in maintenance 6 months later. A real-time system costs you in 24/7 infra forever. Dirty data costs you in retraining cycles. A clever prompt costs you in fragility under edge cases. And full automation costs you when something irreversible goes wrong. The art of AI engineering is knowing where the cost actually lands — and asking early enough to act on it.
More information at the link 👇
