Polish dictated text

Open Settings → Dictation to choose Off or Light polishing. This cleanup uses local text rules and does not make an additional AI request. It changes dictated text; requests to the agent are sent as recognized.

Choose a style

Off keeps the recognizer’s wording. Use it when you want to inspect recognition directly or preserve deliberate repetition.

Light is the default. It removes common hesitation words, reduces accidental repeated words, tidies spacing around punctuation, and corrects a standalone lowercase “i” in ordinary English prose. It preserves some intentional doubles such as “had had” and “very very.”

The English word rules apply when English is the default language. Spacing cleanup can apply in other languages. Light is not a grammar editor and does not understand every spoken correction: “Thursday, no wait, Friday” is not guaranteed to become “Friday.”

Test a change

1. Open a scratch note and dictate a short sentence with the current setting.
2. Switch the polish style.
3. Dictate a similar sentence and compare the inserted text.
4. Keep the setting that best matches your normal writing.

Polishing runs before your replacements. The final result is also the text kept for paste-last-transcript, so re-pasting does not restore the unpolished version.

When a word is wrong

If a name is misheard, add it to Your words. If a phrase should always expand into fixed text, use a replacement. Turn polishing off temporarily to determine whether a problem comes from recognition or cleanup.

For rewritten meaning, tone, or structure, select the text and make an explicit agent request.

Choose cleanup for the task

For informal notes, Light can remove hesitation without a separate rewrite. For a quotation, command, identifier, or deliberately repeated phrase, Off makes the recognizer’s output easier to inspect. Recognition itself can still introduce punctuation or mishear a word, so Off is not a guarantee of a verbatim transcript.

When testing a replacement, turn off unnecessary rules and try its trigger by itself first. Then put the same trigger inside a sentence. This helps distinguish local cleanup from the recognizer failing to hear the words. Keep an example of the actual recognized text when reporting a rule that changes something incorrectly.