Start with reusable categories not specific requests
The mistake I see over and over again in client deliverables is jumping straight into prompts like Write a blog post about the best email marketing strategies for nonprofits. That’s totally fine for one-offs, but it completely falls apart on the sixth request when they say Do another one but for food trucks, and you realize you have to rewrite the entire prompt. Again.
Instead, set up reusable prompt categories based on the actual job — not the specific subject. I usually build three:
– Research and idea generation
– Content structure and outline
– Longform draft builder
Then give each one a blank template prompt and treat it more like a form than a conversation. So I’ll say things like:
“`
Pretend you write for [AudienceType]. You’re writing a [ToneStyle] article that [MainIntent].
Subject: {{Subject}}
Keywords: {{CommaSeparatedKeywords}}
“`
This way you can reuse the prompt every time. The only thing that changes is the subject, tone, or audience — and now it’s easy to plug in new ones because the prompt was built to accept permanent flexibility. Not 💀 hardcoded topics.
Use tags to describe intent not just formatting
People treat prompt templates like Word documents. They get obsessed with inserting headers and emphasis indicators instead of telling the model what the client actually wants. I had this happen so many times with markdown prompts — it would render but entirely misinterpret structure.
The fix was writing meta tags into the prompt content itself. Like literally saying:
“`
# This article is intended to help the reader accomplish [SpecificUserOutcome] using [ToolOrMethod].
# It should sound like a person explaining what they just figured out with clear frustration and real-life steps.
“`
This guides the tone way better than “Use a friendly voice” or “Add headings” ever did. And now I just keep those intent lines at the top of every reusable prompt. It’s like training wheels for each new draft.
Rebuild your prompt with error catching sections
If you’ve ever gotten back a weird hallucination instead of usable output, you know the dread of not knowing what part of your 800-word prompt did it. What finally helped me was breaking prompts into labeled sections — literally with SECTION_A and SECTION_B labels inside the text.
Here’s how I split a longform prompt now:
“`
SECTION_A: Voice and intent
[Custom instructions go here]
SECTION_B: Output goals
You will return an article that includes [X], avoids [Y], and formats [Z]
SECTION_C: Topic generator
Your subject is [ClientTopic]. Think of subtopics that solve real problems.
“`
When something goes off the rails — maybe it added emojis or skipped a section entirely — I can copy-paste and test just SECTION_B with the base model to see if the error is there. It’s like debugging with labeled checkpoints. That saved me hours of diving through full history logs.
Create short prompts for nesting inside other tools
If you’ve ever stuffed a 1,000-word carefully-tuned prompt into something like Make or Zapier, you’ll quickly find out that either the payload gets truncated mid-sentence or that it boots back error 400 for absolutely no clear reason 🙂
So I started splitting every ChatGPT prompt into two variants:
– Expanded version: for writing inside ChatGPT itself
– Compact version: for sending via HTTP or tools that automate
Here’s how they look:
Expanded:
“`
You’re a writer who specializes in SEO content for B2C coffee brands. You’re writing for new customers who don’t understand roast types. The article should explain [Topic] in a way that gently compares two common roast levels…
“`
Compact:
“`
SEO writer. Audience: new B2C coffee buyers. Goal: compare two roast types in plain language.
Topic: {{input_topic}}
“`
Now I just reuse the expanded version for testing tone and structure, and plug the compact version anywhere I need automation. Nesting’s less scary that way.
Force data boundaries so formatting doesn’t leak
This took me forever to catch — I had a prompt that said to add a note inside a blockquote format, but when I ran it through Zapier the response added unexpected blockquote markers inside the content section and outside the wrapper. So my final page ended up looking like:
“`
> This is a note
>> This is not a note but now it is inside a second quote block
“`
Yikes.
The fix was just being aggressive about where and when the formatting lives. I added a single JSON-style table below every request that tells GPT exactly which output field each block of text belongs to.
Like this:
“`
Return your output as:
Title = [short plain text, no formatting]
Highlights = [bullet points, 3–5 lines max]
Body = [Markdown with h2 sections only]
“`
GPT behaves way better with fields like that. Especially if you tell it one field is supposed to be passed into another system. It basically goes into panic mode and gets hyper-specific, which helps 😛
Save tone and style as roles inside your system
One of the biggest misunderstandings with reusable prompts is tone drift. You dial in a prompt to sound “like an annoyed project manager who just fixed a complicated bug” — but the next version inexplicably starts quoting Steve Jobs and talking like a marketing consultant.
The fix is giving your prompt a clear and consistent persona every time. But not in a gimmicky way. A bad example:
“`
You’re an AI expert who speaks like a snarky wizard and uses metaphors about cauldrons.
“`
Better:
“`
You write like a tired developer who is rebuilding something they’ve already explained 10 times, but still wants to help. Simple answers, but short patience.
“`
Even better: saving that as a named instruction set inside your own docs, and referencing it by name. So your prompts could say:
“`
Apply the DEFAULT_WRITER_TONE as used in previous deliverables.
“`
Just like we index colors or spacing in frontend code, naming our tone sets keeps things stable across updates and client rounds.
Store output requests in a readable checklist not dense blocks
Writing instructions like “The piece should be at least 1,500 words, feature three anecdotal examples, include one outbound link, avoid emojis, and prioritize procedural clarity” is just exhausting — and it makes GPT probability rules melt.
So instead I just started writing them like checkboxes:
“`
✅ Word count target is ~1500 words
✅ Use 2–3 personal anecdotes
✅ Add 1–2 natural domain-level links
✅ Never use emoji icons
✅ Show clear procedural steps, not abstract statements
“`
For some reason, this makes GPT way more disciplined. Maybe it’s the illusion of a system test. Either way, if you set up a reusable chunk of these, you can literally paste them into any writing prompt and know the model will stick to the constraints without screaming into the void halfway through. ¯\_(ツ)_/¯
Write the comment you would copy later
Whatever prompt you’re building, just imagine the future you trying to paste that prompt into a comment thread, Slack message, or Notion page to show what you did. Not the one you’d publish for public consumption — the raw version you’ll share in a DM like “Here’s what I used for that one.”
When I started thinking that way, my prompts became clearer, more causal-friendly, and actually reusable without running mental gymnastics two weeks later. Because they made sense when copy-pasted. Not just when run through the IDE-looking GPT textbox.
So whenever I write instruction blocks now, I try to make it simple enough for the version of me who forgot what tool I used last time. That person deserves less stress.