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Create Project Summaries from Meeting Transcripts with GPT-4 and Google Docs

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Created by: Zain Ali || zain104

Zain Ali

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Last update 13 hours ago

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🧾 Generate Project Summary from meeting transcript

Who’s it for 🤝

  • Project managers looking to automate client meeting summaries
  • Client success teams needing structured deliverables from transcripts
  • Agencies and consultants who want consistent, repeatable documentation

How it works / What it does ⚙️

  1. Trigger: Manual or webhook trigger kicks off the workflow.
  2. Get meeting transcript: Reads the raw transcript from a specified Google Docs file.
  3. Generate summary: Sends transcript + instructions to OpenAI (gpt-4.1-mini) to produce a structured project summary.
  4. Convert to HTML: Transforms the LLM-generated Markdown into styled HTML.
  5. Prepare request: Wraps HTML and metadata into a multipart request body.
  6. Create Google Doc: Uploads the new “Project Summary” document into your Drive folder.

How to set up 🛠️

  1. Credentials
    • Google Docs & Drive OAuth2 credentials
    • OpenAI API key (gpt-4.1-mini)
  2. Nodes configuration
    • Manual Trigger / webhook node
    • Google Docs “Get meeting transcript” node: set documentURL
    • AI Chat Model node: select gpt-4.1-mini
    • Markdown node: enable tables & emoji
    • Google Drive “CreateGoogleDoc” node: set target folder ID
  3. Paste in your IDs
    • Update documentURL to your transcript doc
    • Update google_drive_folder_id in the Set node
  4. Execute
    • Click “Execute Workflow” or call via webhook

Requirements 📋

  • n8n
  • Google OAuth2 scopes for Docs & Drive
  • OpenAI account with GPT-4.1-mini access
  • A Google Drive folder to store summaries

How to customize ✨

  • Output format: Edit the Markdown prompt in the ChainLlm node to adjust headings or tone
  • Timeline section: Extend LLM prompt template with your own phase table
  • Styling: Tweak inline CSS in the Code node (Prepare_Request) for fonts or margins
  • Trigger: Swap Manual Trigger for HTTP/Webhook trigger to integrate with other tools
  • Language model: Upgrade to a different model by changing model.value in the AI node