Which transcription model should I use?

Whisper is the common default — it is accurate across accents and languages and available both as a hosted API and as an open model you can self-host. Hosted APIs are simplest to start with; self-hosting makes sense when you have privacy requirements or high volume. For multi-speaker meetings, pair transcription with a diarization step to label who said what.

How do I get reliable action items instead of a vague summary?

Ask for structured output. Instead of "summarise this meeting," request a fixed JSON shape with a summary field, a decisions array, and an actionItems array where each item has a task, an owner, and a due date if mentioned. A defined schema forces the model to separate decisions from discussion and makes the output safe to push into other tools programmatically.

Is it legal to record and transcribe meetings?

It depends on your jurisdiction and often requires informing or getting consent from participants. Many regions have two-party consent laws for recordings. Always notify attendees that the meeting is being recorded and summarised, follow your organisation's policy, and use a tool whose data terms permit the content you are processing.

How do I handle long meetings that exceed the context window?

Chunk the transcript and summarise in stages — summarise each segment, then summarise the summaries into a final note. This map-reduce pattern keeps each call within the context limit and scales to meetings of any length. Keep action-item extraction on the segment level too, then merge and de-duplicate the items at the end.

How accurate are AI meeting summaries?

Good but not perfect. Transcription can mishear names and jargon, and the LLM can occasionally attribute an action to the wrong person or treat a casual remark as a commitment. Treat the output as a strong first draft that the meeting owner skims and corrects, especially the action items, before it becomes the official record.

How to Build an AI Meeting Summariser

A meeting summariser is one of the most satisfying AI tools to build because the pain it removes is universal: nobody enjoys taking notes, and details slip between the call and the follow-up. The pipeline is three clean stages — transcribe the audio, summarise and extract action items with a structured prompt, then deliver the result where the team already works. This tutorial walks each stage, and the generator below produces both the extraction prompt and a pipeline scaffold you can adapt.

Step 1 — Transcribe the audio

Start by turning audio into text with a speech-to-text model. Whisper is the common choice — accurate across accents and languages, available as a hosted API or as an open model you can self-host for privacy. You upload the recording and get back a transcript, ideally with timestamps.

For meetings with several speakers, add a diarization step that labels who said what. Speaker attribution dramatically improves the usefulness of action items, because “Sam will send the deck” is far more actionable than “someone will send the deck.” Always notify participants that the meeting is being recorded and summarised — recording consent laws vary by region.

Step 2 — Summarise and extract action items

The difference between a useless wall of text and a genuinely useful note is structured output. Do not ask the model to “summarise the meeting.” Ask it to return a fixed JSON shape:

{
  "summary": "2-3 sentence overview",
  "decisions": ["..."],
  "actionItems": [
    { "task": "...", "owner": "...", "due": "..." }
  ]
}

A defined schema forces the model to separate decisions from discussion and makes the result safe to push into other tools programmatically. For long meetings that exceed the context window, use a map-reduce approach: summarise each transcript segment, then summarise the summaries, merging and de-duplicating action items at the end.

Step 3 — Deliver the result

The summariser only delivers value if the notes reach people without anyone copy-pasting. Push the structured output to where the team lives — create a Notion page, post a formatted Slack message, or send an email — automatically after each meeting.

Because transcription can mishear names and the model can occasionally misattribute an action, treat the output as a strong first draft. Have the meeting owner skim and correct the action items before they become the official record. Use the generator below to build your extraction prompt and pipeline scaffold, then see how to build a summarisation tool and how to call the chat API for the underlying mechanics.