Canary Corpus for LLM Pipelines: Catch Regressions Before Production Does
Build a production-shaped canary corpus for LLM pipelines with stage-level contracts, noise floors, and release gates that catch semantic regressions.
In a production document-intelligence pipeline that analyzes financial materials, a recent change to the source-attribution rule led to unexpected failures. The new rule required an exact entity-name match in the same sentence, but public documents often used different terms. This stricter instruction caused the model to default to "NONE" instead of identifying relevant context.
The issue went unnoticed until several changes were made, and users encountered missing insights in their briefs. To prevent such regressions, a canary corpus is proposed. A canary corpus is a small, versioned collection of production-shaped inputs and expectations, used to test every output-affecting change before release. It should represent failure surface area rather than just traffic volume.
The corpus is divided into four tiers: baseline items, stress items, incident items, and held-out items. Baseline items are smoke tests for clean, stable examples of important system paths. Stress items cover high-risk combinations and act as the real regression gate. Incident items preserve failure memory, preserving triggering inputs and provenance notes.
Held-out items are unseen test examples for engineers to understand and fix failures. The corpus should be sampled deliberately, overrepresenting inputs where the pipeline makes difficult decisions.
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