Day 54 of 60
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AI-system specific
Output schema gating (structured outputs)
The cheapest cliff in the AI catalog: a JSON Schema, a parse, a retry. Eliminates an entire class of "the parser exploded on weird LLM output" failures for almost zero engineering cost.
ProblemLLM emits drifted JSON; downstream parsers crash; silent data loss.
How it works
Declare a JSON Schema per output. Parse + validate. Reject and retry with the violation list as feedback. Pairs with retry budgets.
What it catches
Format drift, missing fields, type confusion, downstream parser failures. The cheapest cliff on this whole catalog.
Tools
JSON Schema (Draft 2020-12) · OSS OpenAI Structured Outputs · Hybrid Instructor · OSS
Verdict by project size
Small
Rec
Medium
Must
Large
Must
Extra-large
Must
Cost
| Project size | Setup | Maint / mo | Tool / mo | CI / run |
|---|---|---|---|---|
| Small <10k LOC | 4h | 0.5h | $0 | +0.5m |
| Medium 10–100k LOC | 2d | 2h | $0 | +1m |
| Large 100k–1M LOC | 5d | 10h | $0 | +3m |
| Extra-large >1M LOC | 15d | 40h | $0 | +8m |
Setup = engineer-days to first useful run ·
Maint = engineer-hours / month at steady state ·
Tool = out-of-pocket $ / month ·
CI = minutes added (or saved) per pipeline run
Lifecycle & ownership
When in lifecycle
Test Operate Observe
Per merge · Runs after merge to main; nightly heavy jobs.
Who owns it
ML / AI Engineer
Models, evals, drift, guardrails
Collaborates with: Developer, Security / AppSec
Reference implementations
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Instructor examples
Structured-output validation and retry patterns for LLM responses.
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OpenAI Structured Outputs guide
Provider-native schema enforcement for structured model responses.
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Guardrails AI examples
Validation and repair examples for structured LLM outputs.
Quick check
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