A smoother sentence can still be a less accurate sentence. When editing a README, release note, support response, or incident summary with AI, review the meaning before accepting the style.
Disclosure: I work on AI Humanize, a web tool for comparing and refining AI-written drafts. The examples below are written illustrations of a review method, not measured model results.
1. Identify what must survive the edit
Before requesting a rewrite, mark facts and constraints that cannot change: version numbers, commands, dates, units, conditions, exceptions, and the strength of each claim.
For example:
The migration may reduce query time for accounts with more than 10,000 records. It does not change the retention policy.
A rewrite that says "The migration makes queries faster" has lost both uncertainty and scope. A more useful edit would be:
For accounts with more than 10,000 records, the migration may speed up queries. The retention policy stays the same.
The second version is shorter without turning a conditional observation into a promise.
2. Treat code and identifiers as protected text
Ask the editor to leave code blocks, command flags, environment variable names, API paths, and quoted error messages unchanged. Then compare them yourself. A writing model can produce a plausible-looking identifier that your application does not recognize.
A focused instruction is easier to review:
Shorten the prose. Keep code blocks, version numbers, URLs, and error messages exactly as written. Preserve every condition and limitation.
This is an instruction, not a guarantee. Verify the result against the source.
3. Compare before you replace
Keep the original visible next to the revision. Check these questions in order:
- Are names, numbers, units, dates, and identifiers unchanged?
- Did "may" become "will", or "some" become "all"?
- Did the rewrite remove a prerequisite or exception?
- Did it invent a cause, result, benchmark, or recommendation?
- Does the shorter version still tell the reader what to do?
Only after those checks should you decide whether the new wording sounds better.
4. Give one follow-up instruction at a time
If the revision drops an important limitation, ask for that specific repair. For example: "Restore the limitation to accounts with more than 10,000 records. Keep the rest concise."
Small, explicit follow-ups make it easier to inspect what changed. Avoid repeatedly asking for a vague improvement when the actual problem is a missing fact.
5. Keep the final decision with the writer
In AI Humanize, the workflow is to compare the original and revised text, edit manually, and optionally request a follow-up revision. It also supports writing history and TXT/Markdown import and export. Manual edits do not use generation credits.
The same review method works in a text editor or version-control diff. The useful habit is preserving a source of truth and checking each change against it. A rewrite should improve how a technical statement reads without changing what it claims.
What detail do you most often find missing when reviewing an AI rewrite: a condition, a caveat, or an exact identifier?
Top comments (0)