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Automate GPT-4o fine-tuning with Google Sheets or Airtable data

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Who is this for?

Anyone curating before/after text examples in a spreadsheet and wanting a push-button path to a fine-tuned GPT model—without touching curl. Works with Google Sheets or Airtable.

What problem does it solve?

Manually downloading CSVs, converting to JSONL, uploading, and polling OpenAI is a slog.
This flow automates the whole loop: grab examples flagged Ready, build the JSONL file, start the fine-tune, then log the resulting model ID back to a registry sheet/table for reuse.

How it works

# Node Purpose
1 Schedule Trigger Runs weekly by default (change as needed).
2a Get Examples from Sheet Pulls rows where Ready = TRUE from your Google Sheet. Uses the JSONL-Template Sheet as the expected column layout.
2b Get Examples from Airtable (disabled) Alternate source for Airtable users.
3 Create JSONL File (Code) Converts each example to chat-format JSONL and splits into train.jsonl / val.jsonl (80/20).
4 Upload JSONL Uploads the training file to OpenAI (purpose: fine-tune).
5 Begin Fine-Tune Starts a fine-tune job on gpt-4o (editable).
6 Wait → Check Job → IF Polls every minute until status = succeeded.
7a Write Model to Sheet Appends the new model ID + meta to your Model Registry sheet.
7b Write Model to Airtable (disabled) Equivalent logging step for Airtable.

Setup steps

  1. Import & connect credentials

    • Import the JSON flow into n8n.
    • Add your OpenAI API key.
    • Google Sheets: create an OAuth2 credential and link it to both Sheets nodes.
    • Airtable (optional): create a Personal Access Token and attach it to the Airtable nodes.

  2. Copy the template sheet

    • Duplicate the JSONL-Template Sheet linked above into your own Drive.
    • Required columns (exact names):
      | systemPrompt | userPrompt | assistantResponse | Ready |
    • Tick Ready = TRUE for rows you want to include.

  3. Create the registry sheet/table

    • Google Sheet (or Airtable table) named Model Registry with columns:
      Model ID, Training Examples, Epochs, Batch Size, Learning Rate, Finished At.

  4. Tweak model & schedule

    • Change the base model in Begin Fine-Tune if desired.
    • Adjust the Schedule Trigger for daily / on-demand runs.

  5. Test it

    • Mark a few examples Ready = TRUE.
    • Run the flow manually.
    • Check OpenAI for the new fine-tune job and confirm the model ID is logged in your registry.

Resources

Extending the flow

  • Webhook trigger – swap the schedule for a webhook to train on demand.
  • Multi-source merge – enable both Sheets and Airtable nodes to combine datasets.
  • Auto-deploy – save the new model name to an env-var or Secrets Manager for downstream generation workflows.