Why Transcription Prompts Keep Breaking in Multi-Zap Setups

Table of Contents

1. Setting up transcription workflows with GPT is easy until it loops

I was running a podcast workflow where uploads triggered in Airtable, and then bounced to Otter.ai via webhook for transcription. The idea was: once the transcription was done, Zapier would grab it, clean the text, and pass it to ChatGPT with a prompt asking for a summary, pull quotes, and potential social copy.

Worked great for about a week. Then one day, it just… never stopped.

Instead of one clean summary per podcast, I got six. Every time the GPT prompt re-ran, it used *its own output* as the next input. Looped forever until OpenAI hit the usage cap and shut it down. The bug was in a hidden step between cleaning the transcription and feeding it to GPT — a formatter inserted an extra note like “Summary of previous summary” into the cleaned text. GPT interpreted that as a new instruction.

Couple lessons from that:

Double-check if your cleaning steps insert headers or notes — GPT reads all of it.
Don’t store GPT outputs in the same field you read from again later — it’s really easy to confuse the chain.
Zapier Paths can’t tell when a step is using self-referencing input from old runs unless you store run IDs.

2. Using Airtable record triggers introduces invisible timestamp skew

If you’re using Airtable to trigger your transcription automation off a new record or view change, test your timestamps. Airtable’s updated time isn’t the same as CREATED_TIME() vs. LAST_MODIFIED_TIME(), and if you’re using an automation view that filters on something subtle, like a checkbox or tag — the record can appear to enter the view multiple times… even if it doesn’t *visually* change.

In one setup, I had ChatGPT summarize transcripts once a “Ready for Summary” checkbox was checked in Airtable. The trigger worked fine — but it fired more than once because another automation (a Slack notifier) updated that same record with a new Slack thread ID. Airtable saw it as a view-enter event again. Zapier triggered twice.

The fix was dumb: I had to add a separate view where the entry condition was the checkbox AND a formula field that ensured the “Summary Generated” field was empty. No summaries if one already existed. Still missed about one in twenty though.

3. Webhooks returning actual transcript data often hit character cutoffs

When I piped raw transcriptions into OpenAI via Zapier, things worked until — predictably — someone uploaded a 90-minute webinar. The transcript alone was over 11000 tokens. GPT spit back the usual “context length exceeded” error buried in a general 400 error response.

Honestly, the bug wasn’t OpenAI’s or Zapier’s. It was in how the webhook was structured from my transcription provider (Temi, at the time). Instead of returning a direct file link or splitting transcript blocks, their endpoint shoved the whole text blob inside a JSON field named text, which ballooned the payload. Zapier’s “Get text from JSON” parsed it. GPT tried to eat it whole.

Trimmed it down by add-on scripting with a Code step before the GPT send-off:

const full = inputData.transcript;
const preview = full.split(" ").slice(0, 1200).join(" ");
return { short: preview };

Now it gives GPT a sensible chunk. Not accurate for full summary, but enough to pull a hook or intro paragraph to judge whether deeper pass is worth it.

4. Prompt templates break when fields contain invisible line breaks

If your transcription source tool (like Otter, Fireflies, or Rev) outputs as a single paragraph, fine. But if it has speaker labels or timestamps, the exported text sometimes includes invisible unicode characters — zero-width non-breaking spaces and soft return line breaks — that mess with GPT prompt continuity.

One time, GPT was ignoring half the prompt. The same template worked for other transcripts, so it didn’t make sense. I dumped the raw field into regex101.com, and sure enough — a bunch of \u2028 line separators that weren’t showing in the Zapier formatter preview.

I added a Formatter → Text → Replace step targeting \u2028 and \u200B with a space before the GPT block. That fixed it. GPT then stopped acting like the transcript cut off halfway through.

5. AI summaries went stale until I cached the transcription hash

For efficiency, I wanted to avoid reprocessing unchanged audio. The obvious way? If transcript hasn’t changed, don’t re-run the OpenAI prompt.

But Zapier doesn’t let you diff long strings across runs natively. Here’s how I faked it.

Step method used to compare transcripts

In the Zap, after receiving the transcript, I used a small Code block to take a hash: crypto.createHash('sha1').update(transcript).digest('hex').
Stored that hash in an Airtable field attached to that episode ID.
Before the GPT step, pulled the current hash from Airtable and checked for a match.

If it matched, I skipped the AI step with a “Filter” condition. If not, ran it and updated the stored hash. That tiny trick saved me dozens of GPT calls during debugging where the audio file got relinked but not re-recorded.

One odd thing: sometimes the webhook from my transcription provider made line ending changes that altered the hash slightly. So I had to normalize line endings too (strip out \r\n and convert all white space to a literal space) in the preprocessing step.

6. GPT outputs to Notion were cutting off mid-sentence without errors

This one felt haunted. I’d pass a GPT chat summary into Notion via Zapier. Sometimes it worked. Other times it cut off mid-word. No errors, no 413, no clue.

I finally found the culprit in Notion: the Rich Text block Zapier was writing into had a default property character limit. It wasn’t documented anywhere obvious. I only caught it because one version of the summary showed:

“The guest discussed their background in deep machine lea”

Suddenly apparent. It had stopped at 100 characters — Notion’s block property had a hidden limit via the field config on that content database. I’d imported the original table from a CSV, and the field came in as “Short Text.” Manually editing to “Text” (which supports longer entries) fixed it instantly.

Basically: Notion happily lets you send truncatable content into containers too small to hold it, and just eats the rest. Now I double check every relevant field type before pushing AI summaries into it.

7. Adding system role headers stabilizes GPT response structure

After trying too many retries and post-processing regex attempts, I finally added a soft system instruction to ChatGPT:

{
  "role": "system",
  "content": "You are a summarizer returning markdown bullets and hashtags for use in Notion."
}

This tiny change made GPT stop its weird formatting improvisations — like randomly putting hashtags in the middle of bullet lists or adding surprise emojis (which broke Zapier’s text-to-database insert step).

The trick was using the Zapier OpenAI step in “Chat” mode, not “Text” completions. Adding the system role feels optional in docs but actually keeps GPT from slipping into unstructured rants on long transcripts. So anytime I ran into unpredictable structure changes mid-output between identical prompts, this reset it instantly.

8. Error logs from GPT steps are not visible unless step totally fails

This kinda sucks. GPT errors inside Zapier mostly show up as complete failures — but not all of them. Some soft errors (like “content filtered” or “length exceeded”) will yield truncated or null responses that still pass Zapier’s step validation.

One time, I watched a whole batch of Slack posts come through empty. GPT was generating empty responses because the prompt asked it to “extract controversial statements” from a transcript that had profanity. Triggered content filters on OpenAI’s side. But Zapier treated it as a successful step. No error logs. Just an empty string.

You can only catch this by logging outputs somewhere else first — I now pipe all GPT responses into a hidden field in Airtable purely for auditing. That way I can backtrack weird Zap behaviors before they cascade downstream.