When keyterm prompting backfires

Keyterm prompting can improve transcription of specialized names and jargon, but it may also reduce overall accuracy because supplied terms bias the model throughout an entire transcript rather than only where it is uncertain. Similar-sounding keyterms can overwrite words that were correctly recognized, especially proper nouns, product names, and spelling variants, while long exhaustive lists increase collision risks and dilute the influence of genuinely relevant terms. The text recommends using small, call-specific lists based on available metadata, testing identical audio with and without prompts, measuring full-transcript word error rate rather than keyterm hit rates, validating results separately by language, and retesting whenever lists change. It notes that prompting benefits appear strongest in English, that model limits and overflow behavior should be checked, and that pre-recorded requests can combine general context with keyterms despite restrictions in some integrations. As an alternative, contextual prompting and conversation-aware models are presented as more targeted approaches because they use the current discussion and prior interaction to guide transcription rather than globally favoring a static vocabulary.

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