Changing a label broke my workflow
I’ll start with this because this is exactly what made me want to throw my laptop into a lake last Thursday.
I had a really simple Airtable base that I used to send form submissions into a Slack channel and also generate a draft email. It worked perfectly for months. But then I decided to rename one of the field labels — just the display name, not the internal field ID — thinking: “oh this won’t break anything, it’s just cosmetic.” Wrong 🙂
The field was originally called “Category” and I changed it to “Submission Type” so that my new collaborators would understand it easier. The form still worked. New rows still landed in Airtable. But my Zap that watched this form for updates and sent automated responses started giving me super vague “Required field missing” errors.
The clue was buried inside the Zap’s “Find Record” step. It turns out that when you select a field in Zapier, it binds to the field label — not the internal ID. So when you rename it, Zapier doesn’t throw a visible error unless the Zap runs unsuccessfully. Worse, it doesn’t auto-refresh its field mappings unless you go back into the setup and re-select each field from scratch. ¯\_(ツ)_/¯
What fixed it: I reloaded the trigger step, re-selected the new field name, and then re-mapped all the downstream field selections that depended on it. Took about 20 minutes. But I still don’t trust that Zap fully.
ChatGPT remembered the old label name
Classic example of ChatGPT’s training data getting in the way. Because I had changed one label in my schema but not the underlying logic, any time I pasted my updated JSON into ChatGPT and asked for help rewriting part of it, it would confidently reference and suggest code based on the old field name. Like:
“Sure, to access ‘Category’ you can write…”
Nope. That’s not even in the JSON anymore.
At first, I thought it just misunderstood me, so I tried:
“Look again — it’s called ‘Submission Type’ now.”
…and ChatGPT replied with a weird mix of both labels. That’s when I figured out what had happened. This was the input prompt structure I used (bad idea):
“`
You are helping me update a Zap. Here’s the JSON structure from before (below). Based on that, please help me rewrite only the formatting logic. DO NOT change field names:
{ …old JSON pasted here }
“`
So ChatGPT was told *not* to change field names, but it had seen the old JSON structure… and then I gave it a new question referencing the new label without updating the context. Predictably, it clung to the older example.
What I do now: I wrap each prompt with `Forget previous context.` at the top when updating things. Seems basic, but it’s the only thing that reliably prevents hallucinated structure reuse.
Reusing prompt templates caused stubborn responses
I was trying to adapt a ChatGPT prompt that worked beautifully for generating Airtable formulas. It went like:
“Given a list of columns in an Airtable table, write a formula that returns true if [logic]. Include no other text.”
I reused this on a different table where the field names were much longer (like “Total Cost After Tax”) and suddenly the responses were bloated again with explanations, e.g.:
“To achieve this, you can use Airtable’s built-in IF and AND functions…” — completely ignoring the part where I said “no other text.”
I ran the prompt about five times with slight tweaks, then added:
“You are a code-only assistant. Never explain.”
Again: still got explanations.
Turns out, part of my problem was this: my copied prompt already had slight interaction history in the workspace. ChatGPT remembered that I “seemed like” I wanted explanations, even if I said otherwise now. When I opened a fresh chat window, pasted the same prompt, it obeyed exactly.
Lesson: in tasks where you want consistent non-chatty results from ChatGPT, use one-shot or zero-shot strategies in a new thread every time. Otherwise, it acts like a friend who “knows” you want tips even though you literally said “don’t explain.” 😛
Unintended side effects from saving prompt structures
I had a moment where I thought: “Wow, maybe I’ll save time if I turn my best ChatGPT prompts into saved snippets in Raycast.” Save the boilerplate format, reuse it later. Sounds smart right?
Wrong again. When I pulled one of the saved prompts into ChatGPT later, it backfired because I’d copied it during a session where I was debugging webhook timing. The saved prompt included a line that said:
“Assume webhooks fire instantly after record creation.”
…which stopped being true as soon as I added a 2-minute Delay step into the middle of the chain. Now my API output was out of sync, and ChatGPT refused to see why:
“You said webhooks fire instantly.”
Yup. Because past-me locked in an assumption as part of the prompt itself. The fixed version just said:
“Check output against this sequence:
1. Record created
2. Webhook delay
3. External API call
4. Cleanup logic”
Now it stopped trying to interpret the timing assumptions and just worked with the data flow.
A small label like that embedded in your prompt template can sneak in bitterness later. Always reread old prompt snippets the same way you’d reread config files — like they’re lying to you.
ChatGPT refused to format unless I added a workaround
One annoying thing I kept hitting — especially when asking ChatGPT to generate cleaned tables — is that it randomly strips tab spacing or aligns things poorly. I was trying to build a table-like Markdown block so I could paste it directly into Notion.
I’d say:
“Output the data in a Markdown table with columns: Name, Date, Status.”
And I’d get something like:
| Name | Date | Status |
|——|——|——–|
| Alice |June 5| Done |
…but inconsistently, sometimes it would insert extra ` ` or just break the alignment completely. Once it merged the header columns into a single merged cell somehow?!
I got around it like this: I asked ChatGPT to first generate the output in visual layout — like this:
“`
Name Date Status
Alice June 5 Done
Bob June 6 Pending
“`
Then I said, “Convert this to a valid Markdown table but maintain alignment.” That two-step structure got 90% better results, probably because it parsed the intent more clearly before formatting it.
Prompt collapse from too many variables
I was feeding ChatGPT a big chunk of form data and sample outputs to get it to generate summary emails for internal team notifications. Rough structure was:
– Form includes about a dozen questions
– Each field might be optional
– I wanted a short paragraph per submission, humanized
But when I ran this prompt:
“Given this JSON input of form values, write an internal email summarizing the request. Only include fields that are present. Make the tone casual but informative.”
It worked for 3–4 examples. Then — especially when fields were missing — it would just revert to some default paragraph like:
“A new submission has been received with unknown details.”
…which is not helpful at all.
After debugging with smaller payloads I realized it was choking on the shorter edge-case inputs where only 2 or 3 fields were filled. So even though the prompt was clear, ChatGPT saw little to work with and gave up.
What finally worked was changing the prompt to:
“Write an internal email using ONLY the following fields. Do not guess. Do not invent. When a field is missing, exclude it completely.”
And then I passed the payload field by field — not as a full JSON blob, but like:
– Name: “John”
– Reason: “Team need”
– Timeline: (empty)
That format seemed to force it into a smaller scope of creativity and gave way more reliable summaries.
Conflicting tone rules led to middle-of-the-road pudding
This one’s subtle. I wanted to generate client-facing updates in a friendly but professional tone. So I fed ChatGPT a prompt that said:
“Write a progress update email that is friendly, concise, and suitable for clients who may not understand technical terms.”
ChatGPT tried — and the output wasn’t wrong — it just felt like every sentence hedged too much. Like:
“While we are continuing to make progress, some tasks may take additional time depending on scope.”
That sounds like a legal notice.
I realized I was trying to have it both ways: technical clarity and non-tech friendliness. Once I removed the phrase “may not understand technical terms” and instead said:
“Assume the client is busy and skims email, but appreciates candor.”
Boom. Now it wrote:
“We hit one blocker this week due to access issues, but resolved it by Tuesday. Design drafts are now in review.”
Suddenly the tone locked into what I actually wanted. Sometimes being too polite in prompts causes ChatGPT to over-soften the output.
When reused prompts skipped the new important context
Probably the most ridiculous mistake I made: I reused an old ChatGPT prompt that structured Jira updates for weekly logs. Skimmed over the payload I pasted in, assumed it would just work.
But the new project had an entire extra layer of subtasks under each epic. None of those made it into the summary replies from ChatGPT.
That’s because my original template said:
“Summarize tasks from the top-level tickets only.”
So even when the input I gave clearly showed subtasks, it just ignored them. Again: not because ChatGPT didn’t *see* them, but because the instruction ruled them out.
Once I rewrote it to:
“Summarize all ticket activity, regardless of level. Show at least 2 bullet points per epic, even if they’re from subtasks.”
…it suddenly started pulling in the relevant info.
Summary: always re-read your reused prompts like they’re old recipes. Some of them were written for a stove you no longer own.