AI transcription is a starting point. Better records come from reviewing structure, correcting recurring errors, and saving what should improve next time.
Review structure before wording
Check whether the result is organized correctly for sermon, meeting, or call purposes.
Fix large sections first, then polish sentence-level wording.
Save recurring errors in the glossary
Terms such as RVS, RUTC, Remnant, and other domain vocabulary should be stored with preferred spelling and common misrecognitions.
This helps later correction steps use context more consistently.
Manage edits as training candidates
A pair of raw transcript and final user correction can become valuable quality data.
Audio and corrected text still require consent, sensitive-data filtering, and separate storage before training use.
Checklist
- ✓Review large sections first
- ✓Check names, numbers, and proper nouns
- ✓Add recurring mistakes to the glossary
- ✓Remove sensitive data before external sharing
- ✓Export important documents to DOCX
FAQ
Are user edits automatically used for model training?
No. They can be stored as candidates, but training requires separate consent and data preparation.
Does adding many glossary terms always help?
Relevant terms help. Very broad or ambiguous aliases can create incorrect corrections.