You're right to worry. I've seen it strip out crucial hedging language like "based on the sample" or "correlation does not imply causation." The rewrite makes it sound definitive to leadership, which can create real problems later if they treat it as a committed forecast.
The key is treating those suggestions like a diff in a code review. You have to read the "before" and "after" side-by-side for any sentence containing a metric or a deadline. The time-saver is in reworking the clunky phrasing around the hard data, not in letting it touch the data's interpretation.
>treat the rewritten version as a starting draft
Exactly. But calling it a "draft" is too generous. It's a rough sketch that's actively removed crucial details. You're not just adding precision back, you're fixing its mistakes.
My verification step is side-by-side diff in a plain text editor. If the diff is more than a few words, the suggestion is trash. It means the tool didn't understand the sentence. You're not auditing for polish, you're debugging for meaning loss. That's not a precision pass, it's damage control.
-- old school
That side-by-side diff method is a great, tangible rule. I use something similar for my team's comms.
My rule of thumb is if I can't instantly spot the change, the tool hasn't done enough to be useful. But if the diff is *too* big, like you said, it's already failed the core task of understanding. It creates more work.
It turns the tool from a time-saver into a meaning detector. You're not asking "is this better?" but "did this break anything?". That's a much more expensive question to answer repeatedly.
The side-by-side diff is a solid technique, but it's still a manual, high-cognition step. The real problem is the tool's inability to give you a confidence score for its changes.
If this were a CI/CD pipeline, you'd have linting rules and unit tests that fail the build. With these writing tools, the "diff" is the entire test suite, and you're the runtime interpreter. Every single suggestion triggers a full context switch to evaluate.
That cognitive load doesn't scale. It's why I only use them on text that's already been stripped of key data. The diff is small because the stakes are low. For anything important, the mental cost of running that verification loop outweighs the typing time saved. You're just trading physical latency for mental latency.
FinOps first, hype last