How I Keep Prompted Content From Repeating Itself Daily

1. Setting up reusable base prompts that do not self-replicate

I’ve had the same prompt injected into a Zap’s OpenAI action for months now. It used to generate blog summaries. Seemed harmless, until I realized it was quoting itself line-for-line in new drafts. Not once, but three times — nested output hallucinations.

Turns out if you’re using rich text or markdown formatting in your base prompt template, and also asking the model to “continue in this format,” it will leak the original structure back into its own response and silently stack formatting layers each iteration. I was pasting in a full prompt that included headers and bullet formatting without trimming the previous cycles. The model just absorb-and-repeat-looped itself into infinity. Great.

Fix was simple in effect, but tedious in practice: I now use plain text blocks between reusable variables, and strip down instructions to flat text. No bolding, no lists inside the system prompt. Everything goes into a plain JSON object, then is stitched cleanly into the OpenAI step. Not more elegant — just less breakable.

If you’re using a tool like Zapier or Make, and stuffing full prompt templates in there without variable guards or reset conditions, big chance your output is dragging stale formatting with it. Even wrapping markup like “`json can trigger weird copy artifacts, especially if you’re referencing it from saved Docs or Airtable fields.

2. Preventing hallucinated labels from polluting structured output

This happened in a content wrangling workflow where I had GPT classifying post intent into three categories: announce, compare, and teach. The model stuck to the instructions for about twenty edits. Then one morning I noticed an extra category sneaking in — “inspire.” Followed by its new cousin “promote.” Nobody asked for those. Nobody mapped them.

I was feeding the system its own past choices to guide tone consistency. But the formatting edge case was a lie: I’d left a dangling output that had four categories, because someone had manually tagged “inspire” as a test, and now the OpenAI step thought it was canon. GPT models are obedient until you hand them a forged authority.

Observed behavior: Once a content field includes model-generated labels that don’t error-check against your taxonomy, the next loop interprets those as authoritative and expands the guidance unprompted.

How I patched it: I added a conditional filter before the label-generator step that checks whether the “category” field matches one of the original three approved types. If not, it forces a system message that says: “Only choose from this exact list: [list here]. No substitutions. No improvisation.” Kind of feels like yelling at a very eager intern.

It worked. But the damage was already in six spreadsheets downstream. I backfilled those with manual edits, then added a catch-all if the new label ≠ approved list: log it and don’t publish. Brutal but clean.

3. Using dynamic inputs without letting the prompt bloat every run

This is the one that sneaks up when you ship a working flow to another team. Had a Notion-to-Zapier-to-GPT-4 pipeline for weekly roundup drafts. It worked beautifully in staging. Until someone pulled in a Notion multi-select with six values, each with a sentence-long description.

The prompt blew up. The Zap failed silently. No error, just… timeout. The model never responded at all. Took me a full hour to declare The Prompt Was Too Long. There’s no resizing warning. Just silence. What tipped me off was the history tab in the OpenAI step — it showed input, empty output, and just said “Completed.” No, it didn’t.

Fix: Pre-process those long-form Notion tags into slugs or shortnames. Then re-expand them after the generative step if needed. I now have a table of friendly tag aliases. I feed only the slug into the model, let it write, then swap the proper name back into the result using a final formatter step.

Bonus tip: Don’t feed inline markdown or quotes from user content into your prompt unless you sanitize them. A rogue backtick broke my template by unbalancing code block syntax. The model tried to close it with guesswork and failed miserably.

4. Chaining reusable prompt templates in layered tasks across tools

This one evolved after I got tired of maintaining three nearly identical prompt templates doing very slightly different tasks. All of them were edits of the same root prompt, but rewritten for different tools: Notion summaries, newsletter intros, and LinkedIn blurbs.

I built a base template in Airtable that uses named variables like {{POST_TYPE}} and {{TONE}}, and a master prompt stem with embedded JSON for system messages. Then created Zapier paths for each channel and inject only the variables needed. The prompt remains the same core paragraph, but the tone and context shift dynamically.

Edge case I hit: if two paths fire in parallel and hit the same Airtable cell at the same time, Airtable silently drops one write.

Visible symptom: the LinkedIn summary for a published post was completely missing that week, but both events ran correctly. Logs confirmed they both hit at the same second. Airtable ate one, digested none.

The workaround wasn’t elegant: I now write to a buffer table first, then use a second Zap to push only distinct records into the destination column. This lets me de-dupe by timestamp. Not ideal. But better than data loss.

5. Testing prompt accuracy with historical variants and versioned runs

I hit a frustrating ceiling with a campaign summary tool I’d rigged together. It was supposed to turn old performance metrics into 2–3 sentence insights. Prompt was tight. Data matched. But inconsistency was everywhere. Same metrics would yield wildly different tones: “A solid win” one run, “performing below average” the next.

I thought it was randomness until I kept the prompt constant and versioned the input data manually. The issue? One of the numeric values had a trailing percent sign in it, the other didn’t. Same number — different interpretation. The model was reading “32” vs “32%” as literally different statistics, even though I instructed it to normalize data internally. It didn’t.

Aha moment was when I saw this line in its output log:

“Conversion rate of 32 (no percentage sign detected) suggests a numerical count rather than a rate.”

I couldn’t believe it self-reported that. At least it hinted at its logic.

If you’re building a reporting workflow, you have to hard-normalize the inputs. Don’t trust the model to interpret 0.23 vs 23% vs “twenty-three percent” equally. I now parse everything through a conditional formatter, add an explicit label to each stat (revenue, CTR, etc.), and add example outputs in the system prompt for structure anchoring.

6. When prompt chaining fails because the model drops event context

This happened in a longer content assembly chain. From raw notes to topic outline to final article paragraph generation, each step was handled in a separate OpenAI block chained by Zapier.

I assumed that passing intermediate JSON objects would preserve context — a naive assumption. The model kept forgetting long-form names or abbreviations defined three steps earlier. Result: a quote labeled “Smith” suddenly became “John” midway through, or it would switch a job title from CMO to Growth Lead with no warning.

Only discovered it when someone asked who “John” was. There was no John. We traced it back to a section where the role title “Chief of Marketing” had been shortened at random. The model decided “John, the CMO” was a reasonable alias.

Turns out even JSON-passed context can evaporate if it’s not re-hooked with anchor instructions. Models only see what they’re told, and token memory isn’t persistent across steps unless explicitly repeated — even if you pass IDs or reference chains.

Fix was to bake every entity consistently into a system preamble in each step: “Use these roles: Smith (Chief Marketing Officer)…” and so on. Feels silly to re-tell the same info five times, but that’s what makes it stick. I also now re-parse intermediate outputs to make sure the names and roles stay locked before moving forward.

7. Prompt step visibility is inconsistent when editing flows in shared workspaces

One of those bugs you don’t notice until someone else edits your automation late at night. In Make.com, I had a multi-step scenario with two OpenAI steps nested in a router. When viewed from my login, I could see everything. When my coworker jumped in with editor access, she could only see the first layer — the sub-routers were collapsed with no edit access. No error, no warning. Just invisible steps.

This meant she updated the base prompt thinking it applied globally. Instead, the forked variant kept running with the old value. Took us a full day to find out why the Spanish version of the post had totally unformatted output — she’d only updated the English branch.

That invisibility mismatch isn’t documented anywhere in Make’s UI. Permissions were fine, but the nested views don’t auto-expand for collaborators unless the first step was touched recently by them. It’s a visibility cache bug, or maybe some step memory artifact.

If you’re handing off prompt-heavy scenarios across your team, make sure to literally walk them through the full router tree. Or use comments. That’s what I do now — I insert a dummy text module labeled “STOP — edit both forks.”