
AI transcription turns speech into text in minutes and works well as a draft of a meeting, an interview or a voice note. It does not work as final minutes without reading: it gets names, numbers and technical terms wrong, and mixes up who said what when several voices are involved. What makes it accurate or weak is mostly the quality of the audio, not the brand of the tool.
What decides transcription quality
| Factor | Effect | What to do |
| Noise and echo | The number one cause of errors | Record close to the speaker, in a room without echo, and close windows and turn off fans. |
| Several people talking at once | Sentences mixed or lost | Ask people to speak in turn. If the tool separates speakers, check the attribution. |
| Accents, mixed languages and local words | More errors, especially in less common languages | Test with a minute of your own audio before trusting the rest. Tell the tool the language instead of letting it guess. |
| Proper names, acronyms and technical terms | Spelled wrongly or swapped for similar words | If the tool accepts a list of terms, give it one. Otherwise correct by hand. |
| Numbers, dates and amounts | The errors that cost most | Always check against the audio, never against memory. |
A safe method, step by step
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| A transcript is not proof. If the text will support a decision, a contract or a dispute, listen to the original audio at that point. And do not send anyone the transcript as if it were the official minutes without reviewing it. |
| For a useful summary, ask for: decisions made, tasks with an owner and a deadline (only those actually said), and open questions. Tell the AI to write “not stated” where information is missing, instead of filling the gap. |
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SEE ALSO Running an AI model on your own computer with Ollama or LM Studio What happens to what you type into an AI service: reading the data terms |
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