Auto-generate job descriptions from briefing notes with OpenAI and Google Docs Who is this for Recruiters, HR teams, and hiring managers who conduct role briefing conversations and want to convert their meeting notes into polished, structured job descriptions automatically -- without manual copywriting. What this workflow does This workflow watches a Google Drive folder for new briefing documents, extracts structured job data using AI, generates a professional HTML job description, sends it to Microsoft Teams for approval, and exports the final version as a PDF to Google Drive. How it works 1. Trigger -- A Google Drive Trigger detects when a new Google Doc (e.g. a briefing transcript) is created in a watched folder. 2. File organization -- A timestamped subfolder is created and the document is moved into it for clean project structure. 3. Document reading -- The Google Doc content is fetched via the Google Docs API. 4. AI data extraction -- An OpenAI AI Agent analyzes the transcript (supports German input) and extracts structured job data: title, department, responsibilities, skills, benefits, tech stack, and more -- output as JSON. 5. Data logging -- The extracted fields are appended to a Google Sheets tracker for reference and audit. 6. Prompt assembly -- A Code node builds a detailed prompt from the structured data, choosing between "create" mode (first draft) or "revise" mode (feedback loop). 7. JD generation -- A second AI Agent (JD-Writer) generates a complete, styled HTML job description following a professional template with sections like responsibilities, profile, benefits, and diversity statement. 8. Human review -- The draft is sent to a Microsoft Teams chat with an approve/reject form and an optional feedback field. 9. Approval path -- If approved, the HTML is converted to PDF and uploaded to the Google Drive subfolder alongside the original briefing. 10. Revision loop -- If rejected, the feedback is routed back to the JD-Writer for targeted revisions, and the updated draft is re-sent for approval. Setup steps 1. Google Drive & Docs -- Create OAuth2 credentials. Set the folder ID in the Google Drive Trigger node to the folder where briefing documents are saved. 2. Google Sheets -- Create a spreadsheet with columns for all job data fields (jobtitle, department, responsibilities, hardskills, softskills, etc.). Update the Sheet ID in the Google Sheets node. 3. OpenAI -- Add your API key as an OpenAI credential. Used for both the data extraction agent (reads the transcript) and the JD-Writer agent (generates the job description). 4. Microsoft Teams -- Create OAuth2 credentials. Set the Teams chat ID in the approval node to the chat or channel where drafts should be reviewed. 5. HTML-to-PDF -- Install the community node n8n-nodes-htmlcsstopdf (self-hosted only). Add the API credential. Requirements - Community node: n8n-nodes-htmlcsstopdf -- self-hosted n8n only - OpenAI API key (GPT-4 or newer recommended) - Google Drive, Docs & Sheets OAuth2 credentials - Microsoft Teams OAuth2 credentials How to customize - AI extraction prompt -- Edit the system message in the "Extract job data from transcript" node to adjust which fields are extracted or to support different transcript languages. - JD template style -- Modify the prompt in the "Build JD-Writer prompt" Code node to change tone, section order, or formatting style of the generated job description. - Approval channel -- Change the Teams chat ID to route drafts to a different team or channel. - Output format -- Swap the HTML-to-PDF node for a different converter, or skip PDF and use the raw HTML output directly. - Tracker columns -- Add or remove columns in Google Sheets to match your internal job data schema. - Revision depth -- The approval loop supports unlimited revision cycles. The JD-Writer applies feedback minimally without rewriting from scratch.
