Academic writing must represent the question, methods, results, and limitations accurately. Generative tools may reorganize a passage or improve language, but cannot supply missing data, methods, references, or interpretation. Authors remain accountable for the work.
When this workflow is useful
- Turning research records into a manuscript
- Improving clarity and terminology in cross-language writing
- Preparing references, figures, and submission files
A practical sequence
- 01
Build a claim–evidence structure
Connect consequential statements to data, analysis, or reliable literature. Report observations in Results and interpretation in Discussion without silently increasing claim strength.
- 02
Draft from records
Write Methods and Results from laboratory records, code, and statistical output while preserving versions. Complete title and abstract after the argument is stable.
- 03
Manage citations at source
Use a reference manager. Verify every AI-suggested citation, identifier, and whether the original work supports the associated sentence.
- 04
Edit within bounds and review
Accept language changes individually. Protect technical meaning and uncertainty, reconcile values and figures, and follow current disclosure and authorship rules.
Quality-control questions
- Is each factual statement supported?
- Did editing alter meaning or uncertainty?
- Does every citation exist and support the statement?
- Are disclosure, author approval, and confidential-data checks complete?