Before and After: What AI Cleanup Does to Your Dictation

AI dictation cleanup is the difference between dictation you have to edit and dictation you can use immediately. Every transcription in Tap2Talk passes through a Groq LLM that fixes grammar, removes filler words, adds punctuation, and tightens your phrasing — automatically, in under a second.

But what does that actually look like? Here are six before-and-after pairs showing raw Whisper transcription versus the cleaned output.

1. Quick Email Reply

Raw transcription:

hey mike so yeah I got your message about the project timeline and um I think we can probably hit the deadline if we uh push the design review to next week instead of this Friday because honestly I don’t think the mockups are ready yet and it would be kind of a waste of everyone’s time let me know what you think thanks

After AI cleanup:

Hey Mike, I got your message about the project timeline. I think we can hit the deadline if we push the design review to next week instead of this Friday — the mockups aren’t ready yet, and it would be a waste of everyone’s time. Let me know what you think. Thanks.

What changed: “so yeah” and “um” and “uh” gone. “Probably” and “kind of” hedging removed. Run-on sentence split into three. Punctuation added throughout. Every piece of information preserved — the deadline, the design review, the mockup concern, the request for feedback.

2. Slack Message to a Teammate

Raw transcription:

hey can you check the staging environment I pushed a fix for the login bug but I’m not sure if the deploy went through because the CI pipeline was acting weird earlier also the test for the password reset flow is failing but I think that’s a flaky test not an actual bug

After AI cleanup:

Hey, can you check the staging environment? I pushed a fix for the login bug, but I’m not sure the deploy went through — the CI pipeline was acting up earlier. Also, the password reset flow test is failing, but I think that’s a flaky test, not an actual bug.

What changed: One long breath turned into three clear sentences. Punctuation and capitalisation fixed. “Acting weird” becomes “acting up.” Meaning identical. A developer can scan this in five seconds instead of ten.

3. Meeting Notes

Raw transcription:

okay so the main takeaway from the meeting is that we’re going to delay the launch by two weeks because QA found like twelve bugs in the checkout flow and some of them are pretty serious especially the one where the cart total doesn’t update when you remove an item. Sarah is going to own the bug fixes and Tom said he can help starting Wednesday. Oh and we also decided to cut the gift card feature from V1 and push it to V2

After AI cleanup:

The main takeaway from the meeting is that we’re delaying the launch by two weeks. QA found twelve bugs in the checkout flow, some of them serious — particularly the one where the cart total doesn’t update when you remove an item. Sarah will own the bug fixes, and Tom can help starting Wednesday. We also decided to cut the gift card feature from V1 and push it to V2.

What changed: “Okay so” opener stripped. “Like twelve” becomes “twelve.” “Pretty serious especially” tightened to “serious — particularly.” “Oh and we also” becomes “We also.” The facts are all there: two-week delay, twelve bugs, cart total issue, Sarah owns it, Tom helps Wednesday, gift cards deferred.

4. Client Proposal Notes

Raw transcription:

so for the client proposal I want to emphasize three things first the cost savings because they’re currently spending about forty thousand a year on their legacy system and we can bring that down to probably twenty five thousand second the migration timeline we can do it in eight weeks not twelve like the other vendor quoted and third the support package because they specifically asked about twenty four seven support and we offer that at no extra cost

After AI cleanup:

For the client proposal, I want to emphasise three things. First, cost savings — they’re currently spending about $40,000 a year on their legacy system, and we can bring that down to $25,000. Second, the migration timeline — we can do it in eight weeks, not twelve like the other vendor quoted. Third, the support package — they specifically asked about 24/7 support, and we offer that at no extra cost.

What changed: Numbers converted from words to figures. Three points separated into distinct sentences instead of one run-on. “Probably” removed from the cost estimate (the LLM tightens hedging). Structure preserved — first, second, third — but now each point is scannable.

5. Personal Reminder

Raw transcription:

okay don’t forget to call the dentist tomorrow morning to reschedule that appointment also I need to pick up the dry cleaning before six and oh yeah I should email the landlord about the broken dishwasher it’s been like two weeks now

After AI cleanup:

Don’t forget to call the dentist tomorrow morning to reschedule the appointment. Also, pick up the dry cleaning before 6. Email the landlord about the broken dishwasher — it’s been two weeks.

What changed: “Okay” and “oh yeah” stripped. “Like two weeks” becomes “two weeks.” Three tasks separated into three sentences. Even casual personal notes become cleaner and easier to scan later.

6. Technical Documentation

Raw transcription:

the API accepts a POST request to slash API slash v2 slash transcribe with a JSON body containing the audio file as base64 encoded string and an optional language parameter that defaults to English. The response includes a transcript field with the text and a confidence score between zero and one. If the audio is longer than sixty seconds it returns a four thirteen error um request entity too large

After AI cleanup:

The API accepts a POST request to /api/v2/transcribe with a JSON body containing the audio file as a base64-encoded string and an optional language parameter (defaults to English). The response includes a transcript field with the text and a confidence score between 0 and 1. If the audio exceeds 60 seconds, it returns a 413 error (Request Entity Too Large).

What changed: URL paths and field names formatted with backticks. Numbers converted. “Um” removed. “Longer than sixty seconds” becomes “exceeds 60 seconds.” Technical meaning preserved exactly, but the output reads like documentation instead of speech.

What the AI Cleanup Does Not Do

The cleanup is not rewriting your content. It does not:

• Add information you did not say
• Change your meaning or conclusions
• Remove technical details or names
• Impose a different voice — your phrasing stays, minus the filler

It fixes the mechanical problems that come from speaking instead of typing: missing punctuation, run-on sentences, filler words, and hedging language. The substance stays yours.

Customising the Cleanup

Tap2Talk’s cleanup runs through a custom prompt you can edit. Want British spelling? Add it to the prompt. Want bullet points when you list things? Tell it. Want to preserve casual tone and only fix punctuation? You can do that too.

The default prompt handles grammar, punctuation, filler removal, and light restructuring. Most people never change it. But the option is there for anyone who needs specific formatting.

Every Transcription, Automatically

This is not a feature you toggle on. Every transcription in Tap2Talk passes through Groq’s LLM cleanup. Hold your hotkey, speak, release — and the text that appears is already cleaned. No extra step, no extra cost beyond your Groq API usage (roughly $0.04 per hour of dictation).

The total pipeline takes 1-2 seconds: Groq Whisper transcribes your audio, the LLM cleans up the text, and it pastes into whatever app has focus.

Ready to stop editing your dictation? Get Tap2Talk — one-time purchase, no subscription. Or get it free by referring 10 friends.

FAQ

Does AI cleanup slow down the dictation?

No. The LLM cleanup adds roughly 200-400 milliseconds to the pipeline. The total time from releasing the hotkey to seeing text is typically 1-2 seconds.

Can I turn off AI cleanup?

No — and you would not want to. Raw speech-to-text output without punctuation or formatting is harder to use than cleaned text. The cleanup is what makes dictated text usable without manual editing.

Does it work for languages other than English?

Tap2Talk currently supports English only. Multi-language support may come in a future version.