Zenn guide: stop AI summaries misspelling names with 3-step checks

The guide frames name errors as a process gap rather than a skill gap. Its error patterns illustrate how the problem compounds: recognition converts industry terms and company names into common words, summarization then trims names it judges low-importance, and the model can invent names that were never spoken. Because each stage adds its own failure mode, the guide's fix is layered — constraints in the prompt, a reference list supplied upfront, and a check that separates machine detection from human confirmation.

For edge cases, it points to consistency problems that appear as volume grows: the same person's name can shift between kanji variants such as 斎藤 and 斉藤 because transcription converts each segment independently, and industry terms can drift toward similar everyday words ("リスティング" to "リスニング"). The suggested countermeasures are a name list built first, a script that counts variant spellings, and placeholders with timestamps for anything inaudible.

A rough cost example — explicitly labeled as hypothetical, not measured — puts a one-hour audio review at 50 minutes before and 30 minutes after across 10 jobs a month, a saving of about 6,667 yen at an assumed 2,000 yen hourly rate. The guide also notes creating the term list adds about 15 minutes on the first job only.