How to Prepare Meeting Transcripts for AI Summaries
Clean Zoom, Teams and Meet transcripts so AI produces accurate summaries, decisions, and action items.
What makes transcripts noisy
Exported transcripts include timestamps on every line, crosstalk fragments, and filler words. All of it is tokens the model must wade through to find the three decisions that matter.
Clean, then compress
Run the export through the Transcript Cleaner to strip timestamps and filler while keeping speakers. For long workshops, the Context Compressor can protect names and project terms while trimming repetition.
A reliable summary prompt
Ask for a fixed structure:
- Summary in five bullets
- Decisions made
- Action items with owner and due date
- Open questions
- Risks mentioned
Try it free in your browser
- Transcript Cleaner — Removes timestamps, filler words, and duplicate speaker labels.
- Context Compressor — Reduce token usage with compression strategies tuned for LLMs.
Tools mentioned
Frequently asked
Should I keep speaker names?
Yes. Speaker labels let the AI attribute decisions and assign action items to the right people.
How long can a meeting transcript be?
Most one-hour meetings fit comfortably in modern context windows after cleaning, which often removes a third of the tokens.
Keep reading
How to Prepare PDFs for ChatGPT, Claude, and Gemini
A practical guide to extracting clean, AI-ready text from PDFs — born-digital and scanned — so ChatGPT, Claude, and Gemini answer accurately and don't waste tokens on headers, footers, and page numbers.
The Best Way to Feed Long Documents to Claude (and Other Long-Context Models)
Claude's 200K-token context is generous, but you'll still want to clean, compress, and structure long documents before sending them. Here's a step-by-step playbook.
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OpenAI Whisper, Otter, and YouTube transcripts are full of timestamps, filler words, and speaker noise. Here's how to strip them before sending to ChatGPT or Claude — and why it matters.