Setting up the base workflow in GPT
I started this whole mess by trying to build a repeatable workflow that generates meta descriptions for hundreds of blog posts at once. The funny part is it was supposed to be easy. Like, copy some titles and dumps of intro text into a Google Sheet, set up a Zap, call GPT, and boom, hundreds of descriptions in one shot. In reality, it’s tabs everywhere, random drafts piling up, and me staring at errors that only say something like “Output exceeds token limit.” 😛
First thing I did was set up a simple spreadsheet with three columns: Post Title, Draft Text, and Meta Description. Very basic. No complicated formulas. The goal was to feed GPT the title and some context, then get back a meta description that is short enough for search results (around a sentence or two). The tricky part: GPT loves to ramble. Something I had to force myself to do here is add guardrails in the prompt like: “Write a meta description under 160 characters that describes the blog post.” If you don’t, you’ll get back mini essays that just don’t fit.
I tested it by manually pasting queries into GPT, but then I realized how painful it would be if I actually had to generate several hundred. So that’s how I got pulled into Zapier and, eventually, Make — because one of them broke in the middle of the day without warning. Classic ¯\\_(ツ)_/¯.
Wrestling with Zapier triggers and limits
Here’s where the story really started dragging. Zapier will happily let you send rows from a Google Sheet to GPT, but if you’re not careful, it’ll spit out descriptions with extra line breaks, quotation marks, or random filler text. Worst one I got looked like this:
“Here is your requested SEO meta description Please edit as needed your blog post is about workflows and automation enjoy”
It was useless. So I spent half an hour rewriting the Zap step to include stricter wording in the input. I also found out that if you drop the fields from Google Sheets directly into the GPT step, you can control the formatting better instead of just free-typing instructions.
Another fun bug: Zapier sometimes stops mid-run if too many rows hit GPT at once. It doesn’t always tell you why, it just halts. To fix that, I had to “throttle” the automation with a Delay step, so it processed like five rows at a time. Obviously slower, but at least I wasn’t losing chunks of work.
Using Make when nothing else worked
Make, formerly Integromat, feels more hands-on than Zapier, which is good and bad. What I like is you can actually *see* the flow of data from sheets into GPT and back into sheets. It’s built out like a diagram of circles and arrows, so if one step stops, you can tell exactly where it broke.
One thing that annoyed me with Make was the character field issue. Google Sheets doesn’t handle GPT’s new line characters very well, so I was getting meta descriptions stored with big blank gaps. The fix was just to add a Text Parser module inside Make that trims and replaces line breaks with spaces before sending the description back into the sheet. That felt unnecessarily fiddly, but it solved it.
I also noticed Make’s batch handling is smoother. Instead of throttling like in Zapier, it’ll just push rows in smaller bunches without me even asking. Big win there.
Building smarter prompts to avoid junk text
The single most annoying thing about bulk-running GPT is the unpredictability of its answers. Some come back perfect on the first try: short, clean sentence under the character limit. Others come back with stuff like brackets and instructions inside the output. My sheet actually had a row that said “[Insert meta description here].” Useful? Not at all :).
The only reliable fix was to spend time tweaking prompts until they felt idiot-proof. My current working version reads something like: “Write a meta description under 160 characters in plain text that summarizes the blog post.” The “plain text” is key, otherwise GPT sometimes includes quotes or labels. Also, asking “under 160 characters” instead of “about 160 characters” stopped it from writing overly long ones.
I even split the prompt into two sentences because GPT behaves better when you give it explicit, separate instructions instead of long, complex ones. For example:
– One sentence telling GPT the goal
– One sentence saying the LIMIT clearly with a number
That’s what cut down the hallucinated extras.
Dealing with sheet formatting chaos
No one tells you this part, but once you start piling auto-generated text into a sheet, stuff gets messy fast. Rows misalign because new data comes in slower than the sheet refresh. Sometimes cell text wraps weirdly, and you suddenly think the output is too long only to realize it’s just a wrap issue.
I ended up freezing the first row (headers), applying text wrapping to only the meta description column, and also setting a conditional color format that turned the background red if the character count went over the limit. This was lifesaving. I didn’t have to manually count — I could just scan and see which ones broke the rule.
Another hack was creating a helper column that =LEN(cell) to measure how many characters GPT spat out. That column by itself saved me dozens of edits.
Verifying the results for SEO purposes
After all this, I still couldn’t trust every meta description that GPT produced. The system is good, but not flawless. I learned to scan them with a checklist: Does it mention the keyword from the title? Does it actually describe the post or just repeat generic fluff? Is it catchy enough to make someone click?
Sometimes the description missed the obvious main point of the draft text, so I had to redo them by hand. That’s when I realized automation doesn’t replace editing. It just speeds up the first draft creation by about 90%. I’ll happily take that.
When I wanted to compare against what search engines might display, I tested some meta description snippets using random SERP preview tools online. There are a bunch of free ones if you search around. I only used them enough to test the line length, not obsess over pixel widths.
Alternative tools I considered
At one point I almost ditched Zapier and Make entirely to try tools like Airtable automations or even straight up using the OpenAI playground to export results. Airtable’s integration looks clean, but the setup felt heavier, and exporting hundreds of rows wasn’t as simple. The OpenAI playground gave me cleaner formatting, but I couldn’t automate it without hacks.
I also saw discussions on Reddit about people scripting this in Python with the API directly. That would probably be the most reliable version long term, because then you could fully control how the meta descriptions are handled, trimmed, and stored. I just didn’t have the energy to properly write and host a script at the time. Too many tabs open already.
When everything collapses mid run
There was a day when my Zap just silently stopped generating meta descriptions at row 70 of about 500 rows. No warning, no error message, just… silence. I only caught it because I scrolled down and the rest were blank. The task history said “success,” which was bold of them because there was clearly no output. At moments like this, you just laugh and start again. Nothing else to do but babysit the automation while it runs and hope it doesn’t choke for no reason. That’s kind of the reality of these tools — they save you tons of time, until suddenly they don’t.
It left me staring at my spreadsheet at midnight thinking maybe the best workflow would be the boring one where I just paste five titles into GPT and copy outputs back by hand. But then of course I go right back into building the automation the next morning because I can’t let it win 🙂
For anyone getting started — the process is clunky, it’ll break more often than you expect, but once it’s running stable it feels like magic. Until your tabs crash, anyway.