Triple-checking mechanism to guarantee content accuracy
The following protection system is recommended for knowledge errors that may be generated by AI:
- Preliminaries: In the generation settings: 1) Check 'Rigorous Mode' (to reduce speculative expressions) 2) Set up a white list of terminology 3) Limit the number of years the reference has been published (e.g., the last 5 years). For example, medical papers should have 'Clinical Evidence Level Filtering' turned on.
- Process testing: Utilize the 'Contradiction Point Scanning' function of the platform to automatically detect: 1) data inconsistencies 2) conflicting concept definitions 3) contradictions between references and text. For key parameters (e.g. experimental data, statistical results), it is recommended to manually input rather than automatically generate.
- Post-verification: Form a 3-member validation team (mentor + peers + people outside the field) to check: 1) professional accuracy 2) logical coherence 3) readability. Pay special attention to check: a) formula derivation b) translation of proper nouns c) cross-cultural sensitive expressions.
Common risk points to deal with: historical date error using 'timeline checker', geographical information confusion call 'geocode verification', legal provisions updated to enable the 'regulation traceability' function. . Remember: AI has a high error rate in interdisciplinary, emerging fields, and culturally relevant topics, requiring focused manual review.
This answer comes from the articleThousand Pens Writing: an AI tool to assist in completing essay writingThe
May not be reproduced without permission:AI productivity tools " How to prevent intellectual errors in AI-generated papers?
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