7 Things You Need to Know Before Building a Reliable AI System

Seven practical principles for reliable AI systems: architecture, guardrails, recovery, modularity, testing, ownership and measurement.

Before an AI system handles customer records, sends messages or produces business documents, you need to understand how it behaves beyond a successful demonstration.

What happens when a task stops halfway through?

Which rules are enforced before an action takes place?

How does the system know that work has been completed?

Who takes responsibility when the outcome is uncertain?

These questions should shape the implementation from the beginning.

The following seven principles provide a practical framework for building AI systems that are useful, maintainable and suitable for day-to-day business operations.

1.

Your architecture determines how much work the model has to do Model selection is one part of controlling AI costs.

The surrounding architecture determines how frequently you call the model, how much information you send and how much work gets repeated.

OpenAI's cost-optimisation guidance identifies reducing unnecessary requests, minimising input and output tokens, and selecting appropriately capable smaller models as ways to improve efficiency.

Those are architectural decisions as much as model decisions.