What are the main boundaries in a production AI application?

Separates product policy, orchestration, model access, data and tools, validation, state, and operations into explicit application-owned boundaries.

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TL;DR

I separate the user-facing API, product and authorization rules, context assembly, model access, tools or data sources, output validation, and durable state. The application owns identity, permissions, business decisions, and side effects; the model proposes language or structured intent within those limits. This makes failures attributable and keeps provider behavior from becoming the system's authority.

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Practice focus

Whether the candidate sees an AI feature as a distributed application rather than one model call.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

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Core answer
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Overview
The model is one dependency inside a production feature, not the complete application boundary.
I separate the user-facing API, product and authorization rules, context assembly, model access, tools or data sources, output validation, and durable state. The application owns identity, permissions, business decisions, and side effects; the model proposes language or structured intent within those limits. This makes failures attributable and keeps provider behavior from becoming the system's authority.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
For a support assistant, the API authenticates the user, an orchestration layer builds approved context, a model client performs inference, and typed services fetch account data or execute actions. Validation checks the response before it reaches the user, while conversation state and audit records live in application stores. Each boundary has a timeout, failure contract, telemetry, and test surface.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Putting authorization and business policy inside the system prompt.
Treating an orchestration framework as the architecture.
Persisting model output directly as authoritative state.

What are the main boundaries in a production AI application?

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