The Setting I Missed That Broke My Social Media Prompts

Table of Contents

1. Consistency problems with dynamic placeholders in prompt templates

The first time I noticed this was when a short Zap I built to generate a LinkedIn post from a CRM entry started referencing the wrong product name. On screen, all the fields looked right. In the zap history though, the OpenAI output was plugging in data from a different row entirely — like it cached something and didn’t update before composing the prompt. Turns out, when you use variables like {{title}} or {{company}} inside a prompt template, some platforms don’t revalidate them dynamically if the input type changes mid-Zap (especially when switching between paths or Code by Zapier transforms).

So when the CRM feed sent in mixed cases—sometimes with missing fields—those variables didn’t throw errors. They just resolved to stale values from the last successful run. No warnings, no weird logs, just silently wrong posts going out. You won’t catch this unless you dig into the raw logs or remember to include a debug line that echoes all dynamic values before the LLM step. Brutal if you’re scheduling posts automatically.

2. Tiny prompt edits that completely change tone and output

You’d think adding a sentence like “Add a touch of humor” would just make the post a little lighter. But with GPT-4 and Claude, that phrase alone can flip the model’s entire personality — what used to be a snappy CTA suddenly turns into sarcasm or dad-joke territory. The original draft came out clean: “Join us this Thursday to unlock better design thinking.” Then I added that one humor line, and the output became: “Grab your thinking hats — and maybe a latte — for Thursday’s design bonanza.”

Same inputs. Different prompt. Wild new output vibe.

What helped was previewing 3-5 examples with slight prompt variations before putting anything live. And I started tagging each version I tested with version labels in a Notion database linked via Zapier, just so I could track which ones broke tone for which clients. I still don’t have a magic fix here — LLMs over-index hard on directives high in the prompt compared to end-of-text notes.

3. OpenAI token limits cutting off hashtags mid-word silently

This one broke a week’s worth of scheduled Twitter posts. I built the prompt to generate the caption, then “include 3 relevant hashtags and a link to learn more.” When the input text to the model got longer (client descriptions nested in Airtable rich text fields), the model sometimes hit the max token ceiling — but only after generating most of the content. So the hashtags would just trail: “#DataVisua”. No errors, no warnings in Zapier or OpenAI’s logs, just truncated outputs.

You have two bad options here:

Add redundant post-processing steps to validate hashtags — like Regex or a confirm step that checks word endings
Reduce original content size and risk posts sounding too brief

I now inject a “###START###” and “###END###” tag around the intended output section and regex match anything that falls outside that envelope. That way I can catch token cutoffs and even re-run the LLM step with shorter intros automatically if needed. None of that is in any tutorial I’ve seen.

4. Airtable rich text formatting breaking prompt inputs invisibly

If you’re pulling directly from Airtable’s long text fields with rich formatting enabled, you’re likely piping markdown into your prompts without knowing it. Learned this after two clients said “why are those double asterisks showing in our posts?” Turns out *bold*, _italic_, and **headers** are all stored clean visually but transmit as markdown markup in the API to Zapier and Make.

The dumb thing? There’s no toggle in the Airtable API module to pull plain text. You have to run it through a Formatter step to strip out a bunch of possible markdown constructs — and even then, it can miss nested ones.

Here’s the little regex I use now to flatten most of it:

{{InputText}} --> Formatter (Text > Replace)
Pattern: [*_*`#+>-]
Replace: '' (empty string)

That doesn’t catch all edge cases (especially with links or tables), but it gets close enough for prompt clarity. Airtable will likely never fix this — rich text is their thing — so automation folks are stuck doing cleanup.

5. Mapping social channel tone into reusable prompt chunks

Had a week where I was building six different social post variations per blog — LinkedIn, Twitter, Facebook, Instagram captions, email preview, and newsletter blurb. All from the same base copy, but each with tone tweaks. Instead of writing different prompt structures every time, I started saving reusable “tone blocks” as JSON chunks in a library table in Coda.

So now I have things like:

{
  "platform": "LinkedIn",
  "tone": "analytical and actionable, use stats if available",
  "callToAction": "Invite readers to comment with their take"
}

Then I insert these into the system prompt with a simple mapping step before the OpenAI module. This added a layer of reusable control that let me keep the rest of the post logic identical. The surprise bonus? Once the tone and CTA were formally structured, I could do prompt tests much faster by swapping in one block rather than rewriting whole prompt trees.

6. Make modules not escaping quotes inside long-form instructions

This was one of the deepest rabbit holes I’ve fallen into — the ThankYou.ai system, which handles auto-posting to multiple channels, suddenly started returning 400 errors in Make. Took me two hours to isolate it down to a single instruction: an embedded quote in the middle of a sentence. Something like: “Include the phrase ‘customer-first data’ in the caption.”

Turns out, Make’s OpenAI module *does not* escape embedded quotes inside system-level instruction blocks unless you manually double-wrap the phrase. Not a single UI warning. Not in the logs either — the module just fails and says “Bad request.”

Fix was dumb: replace all embedded quotes with Unicode equivalents or just reroute through a Code module that runs .replace(/"/g, '\