How to Handle Schema Versioning for AI Extraction Pipelines

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When building AI-powered data extraction systems, schema changes over time can silently corrupt historical records if not managed carefully. Every extraction row depends on four inputs — the document, schema version, prompt version, and model ID — and changing any one makes new rows incomparable with older ones. Three types of schema changes exist: structural (renameable via pure transforms), additive (requiring a backfill decision), and semantic (where a field's meaning shifts invisibly, making old rows quietly incorrect). Semantic changes are the most dangerous because existing records still validate against the new schema while carrying the wrong meaning, requiring either a full re-extraction or treating old and new records as separate datasets. Storing all four identifiers with every record, including the resolved model ID rather than a provider alias, is essential for diagnosing accuracy shifts and writing reliable migrations.

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ProgrammingDEV Community ·

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