Design the architecture for a production AI application.

Combines explicit trust boundaries, provider access, state, execution modes, resilience, budgets, versioning, and operations into a restrained design.

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TL;DR

I begin with the supported user task, quality target, risk, latency, cost, and data constraints, then choose the smallest execution path that satisfies them. The API authenticates the user, application code enforces policy, a context boundary gathers approved data, and a task-oriented model adapter performs inference. Output is validated before use, durable state lives outside the model, and side effects execute only through authorized application services.

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Practice focus

End-to-end architecture judgment without framework-driven complexity.

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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
A production design must preserve product authority and useful behavior when probabilistic or external components fail.
I begin with the supported user task, quality target, risk, latency, cost, and data constraints, then choose the smallest execution path that satisfies them. The API authenticates the user, application code enforces policy, a context boundary gathers approved data, and a task-oriented model adapter performs inference. Output is validated before use, durable state lives outside the model, and side effects execute only through authorized application services.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
Interactive work uses bounded synchronous or streaming paths, while long tasks use durable operations and queues. Timeouts, retry budgets, concurrency limits, tenant quotas, caches, and graceful degradation protect the service and provider. Traces connect model, prompt, context, tools, validation, outcome, latency, usage, and cost with privacy-aware telemetry.

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

Details

Starting from a framework diagram instead of the product task.
Letting generated output control authoritative state directly.
Adding every resilience pattern without a failure model.

Design the architecture for a production AI application.

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