Why plain transcripts are the wrong format for ChatGPT

Most transcription tools (Otter, Whisper's default output, the dictation in Google Docs) produce a wall of text. ChatGPT can read it, but it has to re-derive when each part of the recording happened and where one topic ends and another begins. On a 60-minute meeting, that re-derivation is unreliable: topic boundaries blur, and the model starts blending decisions taken at different points in the call.

Markdown with explicit topic headings and inline timestamps (## Launch date [00:14:22]) removes the guessing entirely. ChatGPT treats each section as a discrete unit, can quote with a timestamp attached, and answers questions like "summarise everything said about the launch date" in one shot instead of three.

The actual workflow

Open Audio to Markdown, upload your MP3, WAV, or M4A file, click Convert, download the .md file with topic headings and timestamps already in place. Open a new ChatGPT conversation, attach the .md file (or paste inline for short transcripts), and ask your question. For long meetings (60+ minutes), the file attachment route uses fewer tokens than inline pasting.

If you're running ChatGPT Pro or Team with custom GPTs, drop the converted transcripts into the GPT's knowledge base once — every conversation in that GPT then starts with the structured transcript context, no re-uploading required.