One file holds the secret
The single most useful idea I know in AI engineering is this: keep everything that talks to a model in one place. One file, one module, one clear boundary. The rest of your application never calls the model directly. It calls your code, which calls the model.
This sounds obvious. It is obvious. But most AI codebases I have seen do not do it, and they pay for it constantly. The prompt logic is scattered across route handlers. The model name is hardcoded in three places. Retry logic is duplicated. When the model provider changes an API or you want to switch models, there is no single place to make that change.
What lives behind the boundary
The model boundary is the fix. Behind the boundary, you own everything: which model you use, how you format inputs, how you parse and validate outputs, how you handle errors and retries, and how you log what happened. In front of the boundary, the rest of your app sees a clean TypeScript function that takes data in and returns data out.
Why it makes testing easy
This has a side effect that matters for testing. Because the model boundary is a real module with a real interface, you can stub it in tests. You do not need to call the model to test the logic that uses the model. You write a fake that returns a known value, and your tests become fast, deterministic, and cheap to run.
The boundary also forces you to be explicit about what your system expects from the model. When you have to write a function signature that captures the output, you discover ambiguities in your prompt before they reach production. That feedback loop, from interface design back to prompt design, is one of the most valuable things you can build into your workflow.