How I Got GPT to Stop Double-Posting to Instagram and LinkedIn

1. Triggering GPT on a repeating schedule without overlapping outputs

Scheduling GPT to run on a timer sounds easy until it creates two threads in the same minute. Most schedulers, like Zapier’s “Every Day at 9am” trigger, don’t enforce hard execution windows—if the previous run is still going, the next one just kicks in too. So the same prompt gets sent twice. And if you’re feeding outputs directly into a social queue, you get duplicate posts that aren’t identical, which somehow makes it worse.

In theory, you add a timestamp check or a dedupe code step. In practice, deduplication isn’t that clean when your GPT completion varies. I had a LinkedIn post go up at 9:00 and then a slightly weirder version of it publish again at 9:01 because the second run didn’t think the first version “existed” yet. It had been scheduled — but LinkedIn hadn’t acknowledged it. Too late.

What worked (finally) was moving the GPT step after a filename check on an Airtable record. If today’s date entry already existed (and a “posted” field was true), the whole zap halted. This forced GPT to only write once per day, based on a posted flag — not based on time.

2. Parsing GPT outputs into structured captions with predictable line breaks

You’d think putting \n into the GPT prompt would always return consistent line breaks. Nope. Sometimes the model obeys, sometimes you get curly quote bullets and compact text blocks with zero spacing. And when that output gets piped into platforms like Buffer or Hootsuite, which treat line breaks inconsistently, you end up with walls of text or invisible characters.

I got bit when I tried using bolded subheadings in GPT-generated captions by injecting **double asterisks**. GPT wrapped titles just fine — but then I pasted into Instagram via a webhook and lost all markup. Two clients screenshot it assuming it was a copy-paste accident. It wasn’t — it was GPT trying to be helpful.

A few offsets that helped:

• Use \n\n explicitly at the end of every sentence in your prompt. Not just after paragraphs.
• Expect curly quotes or MD-like formatting. Either sanitize with a formatter (e.g. Regex or Make’s Replace module) or force plaintext early.
• Use character counters before API triggers — GPT sometimes overflows Instagram’s 2200-character cap by a few words.
• If you’re crossposting to Twitter/X, push through a second GPT edit to shorten output, don’t just clip original caption.

Eventually I rewired the flow so GPT writes structured JSON keys like “intro”, “main”, and “cta”, then I reassemble them with hard line breaks downstream. More work upfront, zero guesswork later.

3. Handling API rate limits when scaling to multiple brands

Some days this system runs fine across three brands. Other days, LinkedIn rate-limits one webhook and quietly discards the rest. We didn’t find this out until the intern running client approvals asked why nothing had posted in four days. Turns out, LinkedIn API rate limits aren’t globally documented — they exist per app + user combination, and if you send too many payloads in a short span, it starts deciding what to enforce.

Notion’s integration to GPT wasn’t causing issues, surprisingly — but sending too many caption posts from GPT to Zapier to LinkedIn did. Make.com offered better visibility for retries — Zapier just queued quietly and gave a generic error: 429 that never retried.

We ended up staggering posts by brand using a Make scenario with a router and two rate-throttle modules. Hyper-annoying to debug, but it fixed the silent discard issue. The mistake was assuming GPT was to blame when in reality the post-scheduler layer was the leak.

4. Rewriting the GPT prompt until it stops hallucinating hashtags

Here’s how it started: I added “Include 3 relevant hashtags at the end of the caption” into the prompt. GPT complied, sort of. It generated tags like #MarketingVibes and #PostPostmodernism for an architecture firm. Worse, it did it confidently. My fix was to override with a JSON tag list pulled from Airtable — hard-coded per topic and brand-approved.

But even then, GPT would sometimes remix the tags unless I specifically told it: “Do not generate your own hashtags. Use only the hashtags in the provided list.” Without that phrase, it tried to be clever.

Aha moment: I wrote: “Append the following tags as-is, unedited: [‘#design’,’#architecture’]” — and GPT still sometimes split them or added trailing punctuation. Only after switching to JSON keys for hashtags and parsing downstream did it behave.

Keep all formatting logic out of GPT when you care about predictable result structure. Let it write the copy, but use your stack to format and assemble. Every time I tried to blend those functions inside one prompt, it broke again within three days.

5. Getting around Make.com scheduling limitations when GPT needs dynamic inputs

Make’s scheduler trigger is great until you need to dynamically change the prompt contents per time slot. I wanted GPT to write social posts that matched an editorial calendar stored in Airtable — topics vary daily. Make doesn’t let you pass iterator values into the OpenAI prompt field unless you wrap things manually in a JSON transformer. I didn’t know that until the first Monday topic got pasted into every weekday output for two weeks straight.

Once I re-ordered my modules:

1. Make scheduler ran daily
2. Retrieved the Airtable topic where date == today
3. Passed {{topic}} into the prompt field explicitly

…its behavior finally made sense. Before that, Make was inserting static preview data because the iterator hadn’t fired yet. It’s not documented clearly. The order of modules matters more than you’d expect, even with no filtering logic.

6. Replacing your social CMS when GPT means writing a lot more

Here’s what broke: we were using Buffer for three clients. It’s fine for manual scheduling. But when GPT starts pumping out 5 posts a day with slight variations per channel, Buffer becomes unusable. You can’t bulk-edit, reschedule with variables, or organize AI-generated drafts as intelligently as needed.

We hit the wall when an automatically generated campaign wrote 28 posts in a batch — and Buffer posted all of them to the same time slot across multiple days because their import tool ignored the “schedule sequential” toggle. I had to nuke the whole queue and start over after confusing a retail client whose email said “Is this a mistake or avant-garde?”

Eventually we moved all post drafts into Notion using the Notion API, then pushed only final approved ones into Make scenarios that scheduled to LinkedIn and Instagram directly. Zero more queue explosions. Bonus: column filtering by tags meant copywriters could just drag posts into “ready” and everything else stayed untouched.

7. Stopping GPT from predicting dates when scheduling ahead

GPT loves to guess. Whenever you ask it to “write a timely caption for this topic,” it assumes the current date. It’ll say “this Friday” even though you’re scheduling it to go out next month. The worst was when it referenced “last week’s outage” — there wasn’t one. It relied on its training playground, not real-time data.

So I started including a hardcoded pseudo-context prompt in sessions:

“Today’s date is {{2023-05-04}}, and this post is scheduled to be published on {{2023-05-10}}. Respond as if it is May 10.”

That worked… unless the model still injected “as of today” language in its own preamble. The only clean solution? Add the date context, then immediately follow with: “Do not reference the past week, real-time events, or temporal markers.” Small thing — big difference.

If your GPTs are executing inside a queue that runs 1–10 days later, you need that anti-date override every time. Otherwise, your ghostwriter sounds like they’re stuck in a time loop.

8. Avoiding double posts when zaps are retried post-failure

I thought retries wouldn’t re-trigger OpenAI calls. I was wrong. One Slack error (“channel_not_found”) caused Zapier to retry the whole flow — including the GPT step — even though the original post had already gone out via a parallel webhook route.

Zaps don’t checkpoint steps unless you specifically use path branches or formatter dedupe steps. I couldn’t find this documented anywhere, which was frustrating when GPT spat out two versions of a post minutes apart into the same Trello card — one seemed perfectly normal; the second rambling like it had just discovered sentence fragments.

Eventually I checked the Zap history and saw: original trigger ran at 2:01, retried at 2:04, both completed with different GPT outputs downstream. That’s what cost us 20 minutes of deleting echoes from live calendars. After that, I started caching GPT outputs into a row first, then branching all follow-up actions downstream. If the row already had data, GPT stayed out of it.