How to score individual fields in LLM document extraction

Reliable LLM document-extraction evaluation requires field-level scoring rather than a single aggregate accuracy measure, because errors in critical values such as totals, dates, identifiers, and tax details can make an otherwise high-scoring record unsafe for automated processing. Evaluation datasets should preserve the source document, expected labels, model output, and stable metadata, with either one row per field for simple field measurements or one row per document when acceptance depends on multiple fields passing together. Comparison methods must match downstream requirements: free text can use normalized similarity measures, dates should be canonically parsed, financial totals generally require exact decimal agreement, and identifiers must preserve meaningful characters such as case and leading zeros. Normalization and tolerances should only reflect equivalences already accepted by production systems, while omitted values, legitimate absences, invented values, and illegible sources should be recorded as distinct failure categories. Braintrust supports custom scorers, multiple named field scores, document-level acceptance checks, experiment comparisons, attachments for source review, and CI integrations, allowing teams to identify regressions in specific fields and enforce release policies that prevent changes from shipping when critical extraction requirements fail.

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