How would you evolve a structured-output schema safely?

Treats the model-facing schema and downstream consumer contract as a versioned API with compatibility and rollout concerns.

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TL;DR

I treat the schema as a versioned API contract. Adding an optional field may be compatible for tolerant consumers, while renaming a field, changing an enum, or making a field required is usually breaking. I update consumers and evaluation cases deliberately rather than assuming the model can bridge incompatible versions.

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Whether ordinary API evolution discipline is applied to AI-generated payloads.

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Core answer
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Overview
Changing the schema changes both constrained generation and every consumer that parses or acts on the result.
I treat the schema as a versioned API contract. Adding an optional field may be compatible for tolerant consumers, while renaming a field, changing an enum, or making a field required is usually breaking. I update consumers and evaluation cases deliberately rather than assuming the model can bridge incompatible versions.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
For a breaking change I introduce a new schema version, run both paths on representative inputs, and migrate consumers before removing the old one. I keep prompt instructions and examples aligned with the selected version. I also account for provider schema compilation or caching so a rollout does not create unexpected cold latency.

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

Details

Changing enums without checking downstream consumers.
Assuming an optional field is harmless to strict deserializers.
Updating examples but not the formal schema.
Storing outputs without their schema version.

How would you evolve a structured-output schema safely?

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