Why AI Prompt Templates Break in Marketing Automations

1. Prompt inconsistencies across OpenAI and Claude result pages

I had a working prompt template for categorizing leads by intent score, wrapped inside a Make.com scenario, using the OpenAI GPT-4 module. Worked flawlessly for three days. Then Claude got added as a fast-fail backup using an if-error route, and suddenly half the copies were returning generic “I’m not sure how to help with that” answers. Same prompt. Same source data.

The issue isn’t the prompt—it’s the way these two models interpret implied instructions. GPT accepts terse role formats like "Act as a B2B marketer", but Claude hallucinated tone summaries unless the role was grounded with a longer guiding paragraph. OpenAI’s output predictably nested the CTA under a subheading. Claude’s version appended it to the last sentence, or dropped the CTA entirely unless it was explicitly line-separated.

My fix: Use conditional logic downstream to detect the provider, then apply different prompt scaffolds. Dumb, but necessary. Also, Claude doesn’t reliably respect “format in Markdown” unless you add two examples. GPT will format a bullet list off a verbal hint. That seemed minor until I realized Zapier was parsing expected list items by position, and Claude’s version broke the Zap silently on publish. No error. Just no post.

2. Character count overruns collapse Zapier code steps silently

There’s no warning for this. I only found it after exporting run histories and searching for missing outputs. Prompt returned fine, the action runs looked successful, but the blog content never appeared in Notion. Turns out, Zapier silently fails anything over a little past 1000 characters in a Webhook raw body when using variables, particularly nested GPT results.

The bigger surprise: Zapier will show the GPT response fully in the task history, but it won’t warn you that only part of it was passed downstream. That’s worse than a full failure—it fakes it as success. I ended up wrapping the response in base64, decoding it in the next step, then truncating it safely before reassembling the payload. Ugly, but it at least surfaces the error in formatting instead of drop-offs.

Also: if you’re passing markdown-rich content directly to something like Notion or Ghost via Zapier, expect markdown lists to occasionally trigger 400 errors. Turns out these platforms disagree on asterisks vs hyphens and nested list delimiters. But Zapier won’t tell you which key triggered it, just “request failed.” I now regex-sanitize every list item with a quick Formatter step before posting.

3. When auto-rewrites clash with human-reviewed approvals in Airtable

I built a flow that lets marketers approve AI-generated outreach copy via Airtable buttons. Click “Approve,” it writes the version into HubSpot as a ready-to-send sequence. Seemed elegant—until someone clicked approve on three emails in a batch, and only two made it through. One was overwritten. Not deleted—just altered behind the scenes.

The bug came from an automation step I forgot existed: a GPT rewrite trigger that updated a “final tone” field before sendout. But if another user had edited the table while GPT was thinking, the patch action used a stale record revision. So you’d get a silent overwrite based on an old version of the record. No warning. Airtable’s version control doesn’t prevent this. Took me hours to trace.

You can work around this by using a time-based lock field—on submit, freeze the row with a dynamic “in-process” flag that prevents updates until the downstream automation finishes. Or just patch-pause rewrite steps and ask people to wait, which they won’t. Either way, it’s a brittle fix to a logic hole Airtable never flags.

4. Unscoped variables in OpenAI prompt chains create parallel chaos

I had nested prompt calls inside a Make.com router branch, pulling in user bios, generating persona summaries, then feeding those into angle-based email pitches. Everything looked scoped correctly, but occasionally the bio and pitch mismatched—like the “eco startup CEO” getting a pitch about industrial automation.

Here’s the trick: Make lets you reuse variables across routes, but it doesn’t isolate parallel executions unless you wrap each router leg with self-contained scenario logic. Any variable set outside a repeater-loop step remains global to the scenario run. So if two records enter at once with different bio inputs, and the same variable key is reused during token storage, they can overwrite each other mid-run.

Output from Make logs:

{
  "persona_summary": "Tech lead at hardware manufacturing firm",
  "pitch_text": "Some of these materials may appeal to environmentalists"
}

Nonsensical, right? Worked fine in testing when one record ran. Broke once concurrent runs happened. Wrapped every route in a custom function bundle with internal scoping, and the issue vanished instantly.

5. GPT function calls inject system delimiters mid-response unexpectedly

I was mid-debugging a ChatGPT plugin flow where GPT was supposed to create a structured calendar object—returning JSON for a Notion calendar import. It was fine until it returned this:

{
  "event_title": "Quarterly Planning Meeting",
  /* --BEGIN FUNCTION CALL-- */
  "start_time": "2024-06-15T10:00:00",
  "end_time": "2024-06-15T11:30:00"
}

Where did that comment line come from? At first I thought it was a hallucination. But no—this was system-generated, inserted when the prompt instructions suggested both a syntactic function call and a JSON format. Basically, the model tries to be helpful by signaling “function intent” mid-response, even when no plugin is executing the function.

The real kicker was that Notion’s API rejected the object with a syntax error, but the task didn’t fail until 10 seconds later in an unrelated follow-up block. This lag made it almost impossible to trace.

Tweaking the prompt to remove the word “function” entirely fixed it. Also helped to add an explicit header: “Output a plain JSON object with keys: title, start_time, end_time.” Nothing else. No comments, no preamble. GPT tries to fill in what it thinks the function wrapper wants unless you tell it not to.

6. Length-based cutoff resets Claude context on long sequences

This wasn’t in any Anthropic documentation I could find, but I hit it after trying to run a 6-message back-and-forth with Claude using a Zapier plugin setup. The prompt asked for refinements, but after message five, the sixth call acted like the thread had never existed. It defaulted to a polite summary instead of a continuation.

Debugged by logging full message arrays. Turns out, around 13K total characters, Claude’s system truncates the early messages without warning you. It doesn’t error out—it just drops the context. Probably a token cap issue, but the behavior isn’t graceful. GPT throws an error if it overruns token context. Claude just resets the convo and quietly pretends nothing happened.

Got around it by chunking each refinement into an isolated prompt plus context memory injected via a hidden preamble step. Kind of like how RAG works, but cobbled together in Zapier using Format and Storage steps. Not elegant. But better than polite amnesia.

7. Autodetect language setting flips output language mid-campaign

This one actually made it into a live campaign. We were using Google Translate’s API inside Make to localize AI-written ads for EU markets. The initial content was in English, with Translate set to “auto-detect source, translate to FR or DE.” Worked for about 20 records a minute. Then started getting German outputs in a French flow. Entire ad sets mislabeled.

If the English source sentence included a borrowed French word or cultural noun—like “rendezvous” or “atelier”—Google would sometimes detect French as the source. So then the “translate to French” step… did nothing. Returned the original. Meanwhile, the German step translated that same pseudo-French back into German. So same content went two ways.

Fixed it by explicitly defining source as “en” and stripping non-ASCII markers pre-translation. Added a debug log step showing source detection response. Shouldn’t have had to, but turned out this behavior isn’t a bug—it’s the expected fallback. Knowing that would’ve saved an hour of QA panic.