Setting up the initial automation
The very first time I stitched GPT4 into my podcast workflow, it felt like magic. I had a new episode sitting in Dropbox, I connected Zapier to watch the folder, and as soon as the file landed, it sent the transcript request out to OpenAI. At least, that was the plan. In practice, I had three tabs open trying to remember if I had actually saved the step that pasted the transcript into Google Docs. The error messages didn’t say much either, just the equivalent of “Something went wrong” which is not helpful when you’re staring at a blank document at 1 AM. 😛
The trick was to get each step small and dumb-proof. I literally renamed each Zap step like “Step1 File Arrives” instead of the default naming because later, when you hit a bug, you have no idea which vague step description is throwing the error. If you’re brand new to automation, treat naming steps the way chefs treat labeling spice jars: you think you’ll remember later but you won’t.
Also, if you ever find that the Zap is not triggering when the file uploads, check the simplest thing first — whether Dropbox is tracking the right folder. I have lost hours because I moved a file into a subfolder and the entire train collapsed quietly. That silence is worse than a red error message because at least an error means you know something tried to run.
Getting transcripts without breaking everything
So the transcript part is always messier than it looks. I tried pushing the raw audio straight into OpenAI before realizing large files choke if you don’t compress them first. Suddenly, the Zap just fails without a warning. Here’s what I learned: run your audio through a simple compressor (there are free apps out there that just shrink file size) before the step sends it for transcription. Do this once and you avoid the “file too large” mess that eats up credits without giving anything back.
Another weird bug you might hit: sometimes the same episode triggers twice. You’ll open your Google Doc and see two identical transcripts glued together. That happens when your folder watcher is set to trigger on file edit as well as file create. If you cut the upload halfway and it resumes, Zapier thinks “oh cool a new file” and kicks off again. Bit of a facepalm moment. 🙂
For cleanup, I also add a tiny step at the end that strips blank rows, because AI transcriptions often return long stretches of empty lines. Without that, the summary step later just rambles more than it needs to.
Writing the summary with GPT4
Once a transcript makes it through in one piece, it goes straight into GPT4. This is where you actually get the text you care about: the summary. I started with prompts like “Summarize this podcast” but the results were so generic they might as well have been horoscope blurbs. The fix: tell the model *how* you want it written. For example, say “Make a summary in 5 short paragraphs, keep technical terms, and highlight any guest names clearly.” That’s the difference between a usable blog post draft and something that looks like filler text.
I also had to cap how much transcript GPT4 sees at once. Uploading an entire hour-long conversation can push too many tokens through in one shot, so instead, I broke the transcript into chunks (like per 10 minutes) and ran multiple summaries, then stitched them together. Yes, it’s messy. Yes, you’ll have lots of tabs open. But the payoff is summaries that actually reflect what was said instead of the AI hallucinating something about “innovation” or “dynamic trends.” ¯\_(ツ)_/¯
Don’t be afraid to literally paste a transcript chunk into the GPT playground once or twice to test what style prompt works before you set it into automation. I ruined an hour debugging a Zap only to find out the real issue was just the wording of my instructions.
Cleaning the text before publishing
AI summaries look fine until you scroll down and see whole paragraphs that just don’t belong. Sometimes it copies weird stage directions like “[laughs]” or timestamps. So before the text heads into Notion or Google Docs for editing, I drop in a simple filter that removes anything inside brackets. That alone makes the draft usable.
Another pain point is formatting. GPT loves bullet points, but if your publishing platform prefers plain text, you’ll get unexpected spacing problems. I had a Zap push summaries into Notion and the bullets turned into random indents that I had to manually delete. Easy fix: just tell GPT to return plain paragraphs instead of lists. Sounds trivial, but forgetting to specify that will double your cleanup time.
And if your automation suddenly delivers empty text, check if the GPT step silently timed out. I saw that happen more than once when I fed it back-to-back large chunks too quickly. I had to insert a short delay action between API calls to stop overwhelming the system.
Delivering summaries where they are needed
The last step is sending the cleaned summary to a place where your team or audience will actually read it. I tried three different routes: posting into a Slack channel, creating a new Notion page, and emailing a summary to myself. Each one broke in its own unique way. Slack rejected long text blocks and just chopped them. Notion credits ran out in the middle of the month and my automation froze like a cat in headlights. Gmail was the only thing that consistently worked, though it made my inbox feel like an overfed parrot repeating episodes back at me.
If you want something more automatic, try routing summaries into a WordPress draft. Platforms like WordPress make it possible to feed directly into your site, though you have to format carefully or HTML tags get scrambled. The most stable setup for me has been cleaning the summary in Docs first, then routing a finished doc URL to whichever platform needed it.
Small failures that cost big time
I have to mention the silent resets. For reasons I still don’t fully get, sometimes Zapier just resets a connection and doesn’t tell you until nothing runs. This happened with my Google Docs connection: one day the Zap started failing with “permission denied.” I reconnected the account and it worked like nothing happened, but I had already lost two days of summaries that never got created. That kind of bug is invisible until you realize your whole backlog vanished.
Another failure mode is when transcript services change how they return data. I once had everything working, but suddenly the API started labeling chunks differently, and my Zap couldn’t find the field where the text lived. My doc came out blank for a week before I noticed. Always test weekly, even if you think it’s stable. Automations rot faster than bread.
How to keep the workflow alive
After rebuilding this flow so many times, the two biggest lessons are: always label your steps clearly, and assume things will break quietly in the background. I now keep a manual override where I can trigger a one-off run by pasting audio into GPT directly if automation dies. That backup saves you from panicked late nights.
If you’re tempted to overcomplicate it with fancy add-ons like scheduling or auto-social posts, wait until the basic flow is bulletproof. At least get the file to doc to summary move working 10 times in a row without crashing before you pile on new triggers. Otherwise, the whole tower tips over the first time someone uploads an episode with an underscore in the filename. 🙂
The smoother this gets, the less you’ll dread uploading a new episode. At least until the APIs silently shift again and everything trips over itself, which is pretty much the rhythm of this hobby.