How would you design graceful degradation for an AI feature?

Defines safe fallback, reduced capability, stale or cached behavior, and feature disablement without hiding changed guarantees.

1Say itAnswer out loud first

2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

I list the feature's essential guarantees and decide what remains possible when the model, retrieval, or a tool is unavailable. Degraded behavior may use deterministic search, a smaller approved model, a cached result with a freshness label, a queued response, or a clear temporary-unavailable state. The user is not shown a lower-confidence answer as if nothing changed.

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Practice focus

Fallback semantics, user honesty, safety, dependency isolation, and product continuity.

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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 fallback is useful only if it preserves the guarantees the user and application still rely on.
I list the feature's essential guarantees and decide what remains possible when the model, retrieval, or a tool is unavailable. Degraded behavior may use deterministic search, a smaller approved model, a cached result with a freshness label, a queued response, or a clear temporary-unavailable state. The user is not shown a lower-confidence answer as if nothing changed.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
Fallback is selected by failure type: a model outage differs from missing evidence, invalid output, or an authorization dependency failure. High-risk actions fail closed, while low-risk assistance can narrow its scope. I test fallback paths regularly because code that runs only during incidents is likely to be stale.

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

Falling back to an unevaluated model automatically.
Serving stale content without identifying its age.
Using the same fallback for every failure.

How would you design graceful degradation for an AI feature?

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Overview

Review signals

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