Common Identification Problems
Factors such as colloquial expressions, background noise, and jargon may affect the accuracy of AI extraction.
Accuracy Improvement Program
- Pre-processing optimization: use noise reduction software to process recordings before uploading, or manually organize the text of key discussion points
- Feedback training mechanism: mark "reject" for incorrect extractions and add correct expressions to improve model learning.
- Multi-mode calibration: Simultaneous uploading of audio + text records, system cross-validation to improve accuracy
Technical supplements
- Keyword enhancement: explicit use of trigger words such as "action items" and "TODO" in meetings
- Domain Adaptation: Custom dictionaries can be added to SnapLinear settings for specialized terminology
- Manual review process: Establishment of "AI initial screening → executor confirmation → supervisor final review" three-level checking
This answer comes from the articleSnapLinear: an AI tool that automatically generates Linear tasks from meeting notesThe
May not be reproduced without permission:AI productivity tools " How to overcome the accuracy problem of AI extracting action items from meetings?
