When reviewing an AI system architecture, teams often expect the initial focus to be on model selection: which model, how large, or how benchmarked it is. Instead, five foundational questions actually decide whether an AI system will succeed in production.
In production engineering experience, when an AI system falls apart later, it's almost never the model's fault. It failed one of these five.
1. Source: where does the truth actually live?
Forget the database. Forget the embeddings. We want to know where the real information lives โ the source everything else is copied from.
Is it trustworthy? Is it current? And the question that ends most conversations fastest: once this system is live, whose job is it to keep that source accurate?
Silence usually follows. Silence means there isn't an AI project yet โ there's a data problem wearing a nicer outfit.
2. Fallback: what happens the moment it's wrong?
No model is 100% accurate. When an output is wrong or low-confidence, what happens? Does it fail silently, hallucinate a number, or route gracefully to a human or rule-based fallback?
3. Trace: can you replay an answer end-to-end?
If a client or auditor challenges an answer generated yesterday, can you trace the exact prompt, retrieved chunks, and model version used?
4. Test: does your eval suite represent real users?
Benchmarks and synthetic tests are easy. Production user inputs are messy, edge-case heavy, and unstructured.
5. Kill switch: who can turn it off tonight?
Eventually something goes sideways: a model updates without warning, a source gets poisoned, or an edge-case loops. Who has the authority and access to disable the feature safely?
The five, at a glance
- Source โ Where does the truth live, and whose job is it to keep that accurate?
- Fallback โ What happens the instant the system is confidently wrong?
- Trace โ Can you replay any single answer, end to end?
- Test โ Does your eval set look like real usage, or just a leaderboard?
- Kill switch โ Who can shut it down tonight, and are they reachable?
Auditing an AI System for Production Readiness?
If you have an existing AI pipeline experiencing edge-case failures, latency spikes, or hallucination risks, let our engineering leads audit your architecture, data pipelines, and evaluation suite.