The AI architecture review checklist
24 questions to answer before you build an AI/LLM feature. Catch cost, latency, hallucination, and data-leakage risks before they become production fires.
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What’s inside
1. Use-case clarity
Define the job the AI does, the human-in-the-loop handoff, and the failure modes that matter.
2. Model selection
When to use frontier models, open-weights, fine-tuning, or small local models.
3. Context architecture
RAG vs long context vs fine-tuning: which fits your data freshness and privacy needs.
4. Cost & latency
Estimate token spend per user, set p99 latency budgets, and plan caching/cost controls.
5. Safety & compliance
Hallucination mitigation, prompt injection defenses, data leakage guardrails, and audit trails.
6. Evaluation & launch
Build an eval suite, set launch gates, and monitor production behavior from day one.