1. Opening Problem Statement
Meet Alex, a passionate philosophy podcast host who spends countless hours manually transcribing episodes, pulling out key topics, and generating engaging questions for listeners. Each time a new episode launches, Alex loses an estimated 4-5 hours refining transcripts and crafting a summary and discussion points—time that could be better spent creating content or engaging listeners directly. The manual process is error-prone, tedious, and often delayed, leading to lost engagement opportunities and less professional communication to the audience.
This blog dives into a unique n8n workflow called “Podcast Digest” that revolutionizes Alex’s podcast production line by automating transcript summarization, topic extraction, and rich question generation using OpenAI’s capabilities—all sent quickly via Gmail. If you’ve ever wished you could automate podcast content digestion and share insightful, ready-to-read digests with your audience effortlessly, this article is for you.
2. What This Automation Does ⚙️
When the “Podcast Digest” workflow runs, it automates the entire content crunching pipeline from raw transcripts to polished, shareable digests. Specifically, it:
- Accepts a transcript of a podcast episode as input from a manual trigger.
- Splits the transcript into manageable chunks for effective processing.
- Uses OpenAI models (GPT-3.5 and GPT-4) to summarize the content and extract central topics.
- Generates thought-provoking questions related to each topic to ignite listener discussions.
- Researches topics further by querying Wikipedia and providing detailed explanations.
- Formats the combined summary, topics, and questions into an HTML email sent via Gmail to the distribution list.
Ultimately, this workflow can save Alex 3-5 hours per episode, reduce human error, and create an engaging, professional podcast digest automatically.
3. Prerequisites ⚙️
- n8n account with workflow execution access.
- OpenAI API account with access to GPT-3.5 and GPT-4 models for summarization and topic extraction.
- Gmail account with OAuth2 credentials for sending formatted digest emails.
- Wikipedia Node integrated for topic research.
- Basic understanding of JavaScript (for the Code nodes) and workflow logic.
Optionally, you can self-host n8n using a service like Hostinger.
4. Step-by-Step Guide to Build the Podcast Digest Workflow 🔧
Step 1: Setup Manual Trigger to Start Workflow
Navigate to n8n dashboard, then add a manualTrigger node named “When clicking “Execute Workflow””. This node initiates the workflow manually when an episode transcript is ready.
You should see the manual trigger node ready for execution.
Step 2: Add Code Node for Transcript Input
Add a Code node named “Podcast Episode Transcript” right after the trigger. Paste the lengthy transcript text inside this node’s JavaScript code block under return { transcript: `...` }. This node simulates receiving the transcript as structured data.
Common mistake: Avoid exceeding character limits — using chunked loading later helps.
Step 3: Convert Input to JSON Document
Insert the “Workflow Input to JSON Document” node of type @n8n/n8n-nodes-langchain.documentJsonInputLoader with the pointer set to /transcript to prepare the transcript for AI processing.
Step 4: Split Transcript into Chunks
Add the “Recursive Character Text Splitter” node (@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter) to split the transcript into ~6000-character chunks with 1000-character overlap for context preservation.
Step 5: Summarize Transcript
Add the “Summarize Transcript” node using GPT-3.5 or GPT-4 with a refine type summarization chain. This node generates a comprehensive, digestible summary from the chunks.
Step 6: Extract Topics and Questions Using GPT-4
Add “Extract Topics & Questions” node (@n8n/n8n-nodes-langchain.chainLlm with GPT-4) which takes the summary to produce a list of high-value topics and related thoughtful questions.
Step 7: Parse the Structured Output
Use “Structured Output Parser” to enforce the correct JSON schema for questions and topics extracted with their explanations.
Step 8: Split Topics Out for Research
Add “Topics” node (itemLists) to split topics for parallel Wikipedia research.
Step 9: Research Topics Using Wikipedia and OpenAI
Connect the “Topics” node output to “Wikipedia1” tool and then to “Research & Explain Topics” node (AI Agent). This fetches detailed information about the topics from Wikipedia and enhances it with OpenAI GPT-3.5 multi-turn conversation keeping the transcript context.
Step 10: Format Result as HTML
Add a Code node named “Format topic text & title” where you combine the summary, researched topics, and questions into HTML format using JavaScript to prepare it for email.
Step 11: Send Digest Email via Gmail
Finally, add the “Send Digest” node of Gmail type with OAuth2 credentials. This sends the beautiful HTML digest to your desired inbox or mailing list.
5. Customizations ✏️
- Customize the manual trigger to be a webhook for automated transcripts submission.
- Adjust text chunking parameters in the “Recursive Character Text Splitter” node to optimize summary quality.
- Switch GPT model versions or tweak temperature settings for more creative or more factual summaries.
- Modify email templates in the “Send Digest” node for branding or different recipients.
- Add new research nodes or integrate external APIs beyond Wikipedia for richer topic insights.
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6. Troubleshooting 🔧
Problem: “Transcript too long for a single API call”.
Cause: OpenAI’s token limits.
Solution: Use the Recursive Character Text Splitter node with chunking and overlap to feed smaller segments.
Problem: “Gmail OAuth2 authentication fails”.
Cause: Invalid or expired Gmail credentials.
Solution: Refresh or reauthorize your Gmail OAuth2 credentials via n8n credentials page.
7. Pre-Production Checklist ✅
- Test manual trigger to confirm the workflow starts.
- Verify transcript input is correctly formatted in the Code node.
- Confirm OpenAI API keys are active and have sufficient quota.
- Check Wikipedia node connectivity.
- Send test email through Gmail node and verify delivery.
8. Deployment Guide
Activate the workflow in n8n after all nodes are correctly configured. Schedule manual or webhook triggers as needed. Use the n8n execution log to monitor workflow runs and quickly resolve failures. Optionally, run workflow on your own self-hosted instance for control and scale.
9. FAQs
- Can I use other email services? Yes, you can replace Gmail with SMTP, Outlook, or SendGrid nodes with appropriate credentials.
- Does this consume many OpenAI tokens? Summarization and topic extraction are token-intensive; budget accordingly.
- Is my podcast transcript data secure? Your data is processed only through your n8n instance and the OpenAI API. Use secure credentials and HTTPS.
10. Conclusion
By setting up this dedicated “Podcast Digest” automation in n8n utilizing OpenAI’s GPT models and Wikipedia integration, Alex can now transform lengthy podcast transcripts into insightful summaries, topic breakdowns, and engaging questions automatically and efficiently. This process saves several hours per episode, reduces manual errors, and boosts listener engagement with thoughtful content delivered directly via email. If you’re ready to elevate your podcast workflow, this workflow is a powerful starting point, with plenty of scope to customize and expand.
Consider extending this workflow to automatically post digests on social media, create blog posts, or even generate audio summaries using text-to-speech APIs. Keep experimenting and happy automating!