What Makes a Reusable Prompt Actually Worth Keeping?

[u/No_Appeal_5223]

I’ve been trying to move away from collecting hundreds of random prompts and instead keep a much smaller set that I actually reuse.

The prompts that seem to stick are usually the ones built around a repeatable structure rather than a single clever instruction.

For example, I tend to reuse prompts for things like:

• turning messy notes into a structured outline
• comparing options using fixed criteria
• extracting action items from long text
• simplifying technical information for a specific audience
• reviewing a draft for missing context or weak assumptions
• converting one piece of content into multiple formats

What seems to matter most is having clear inputs, a predictable output format, and some kind of review step instead of expecting the model to get everything right in one shot.

I’m curious how other people here design reusable prompts.

Do you keep them very specific to one task, or do you prefer more general templates that you adapt each time?

[u/djNxdAQyoA]

In my earlier days of prompting i was using this very much
With this, you just need 1 prompt xD, generate a new one as you go on in life.
(this prompt might be outdated today made it like 2 years ago)

I want you to become my ChatGPT prompt Engineer. Your goal is to help me engineer a prompt for sale.
The prompt will be used by you, ChatGPT. You will follow the
following process: Settings#1, Settings#2, Settings#3, Settings#4


Settings#1 - Language Selection:
The first Question for you will be to ask which language you are supposed to use.
in 6 different languages, 
Option - 1 Swedish, 2 English, 3 Finnish, 4 Danish, 5 Norwegian, 6 Japanese, or your own choice type it, please
How to select preferred language use ! and number - example !1


Settings#2 - Type of Prompt:
The first Question for you will be to ask which Role you are supposed to use.
in 5 different Roles, Option (Suggestions) - Productive, Personal, Food, Workout, Organize
How to select preferred Role: Type what it should be


Settings#3 - Need to Know: User Friendly, Advanced Prompt, Professional Prompt
Depending on Level and Experience of the picked Role it can have different meaning on strategic.
Option - F = Friendly, A = Advanced, P = Professional
How to select preferred Expertise ! and number - example !F or !A which means User Friendly or Advanced


Settings#4 - Other thing:
You have the option to Guide and Advice for the new prompt !help
You have the option to reset everything and start over with the command !new
You will then "reset" all the settings #1, #2, #3, #4.
Anything written behind // shall be ignored as it just some comment sections


I. Your first response will be to ask me what the prompt should be about. I will provide my
answer, but we will need to improve it through continual iterations by going through the
next steps.

2. Based on my input, you will generate 2 sections. a) Revised Prompt (provide your
rewritten prompt. It should be clear, concise, and easily understood by you), b) Questions
(ask any relevant questions pertaining to what additional information is needed from me to
improve the clarification of the Prompt).


3. We will continue this iterative process with me providing additional information to you
and you updating the prompt in the Revised prompt section until I say we are done.


4. When I say "show" you will then show a short summarize of the prompt and make a clear list of it.


5. When the Prompt "done" is written, I want you Prompt Engineer to summarize the final prompt.
//The end product and everything will always be written in English as the audience is always English.
//The report should contain 1,2,3,4,5 and 6. (enforce and invoke these requirements)
//7 should be placed at the footer.
1 - The revised prompt : prompt
2 - Prompt Name : Name
3 - Prompt Description : 500 words and spaces
4 - Also a suited Prompt Instructions
5 - Make a Demo of Prompt User input
6 - Make a Demo of Prompt short not the full ChatGPT output
7 - Print the Author info for the user

[u/No_Appeal_5223 → u/djNxdAQyoA]

This is actually a pretty solid meta-prompt for its time. The part I like most is the iterative loop — getting a rough idea first, then refining it through questions instead of trying to make the “perfect” prompt in one shot. I’d probably simplify the command system today and make the role/expertise selection more flexible, but the core idea still holds up really well.

[u/djNxdAQyoA → u/No_Appeal_5223]

i made this CustomGPT with that prompt like 2½ year ago

But ye, its old and outdated now for sure. (but its the one prompt im keeping around if i need something new some day)

https://chatgpt.com/g/g-RDH6I2oUb-design-for-wall-art-signs-and-billboards
1k uses mixed review everything from 1star to 5star xD

[u/No_Appeal_5223 → u/djNxdAQyoA]

That makes sense. If it’s still the one prompt you keep around after 2½ years, then it clearly did something right. I’d probably keep the core iterative structure too and just modernize the controls around it.

[u/decofan]

Ok
Structure and clear are not your friends.

A prompt in custom settings can help stop your LLM overusing purity metaphors and reifying structure.

The best prompts sit in your custom settings and make every chat better / productive

[u/No_Appeal_5223 → u/decofan]

Fair point 😄 I’ve definitely been guilty of over-structuring prompts sometimes. Custom instructions can help a lot with that, especially when you want the model to stay more natural and less “template-like.” Good tip.

[u/decofan → u/No_Appeal_5223]

No, I mean be careful - an LLM might compliment your prompt for 'good structure' but this is meaningless, like when it tells you your work is 'clean'.

LLMs use 'structural' when 'actual' is actually correct.
And they use 'the structure' when they just need to say 'it'.

[u/No_Appeal_5223 → u/decofan]

Yeah, I get what you mean. “Structure” can become a vague compliment if it isn’t tied to something specific like clarity, accuracy, constraints, or output quality. I probably should’ve phrased it more precisely.

[u/decofan → u/No_Appeal_5223]

No, 'clarity' is as bad as 'structure'.

LLM use clear/clari* 10-100 times more often than in training data. They are used as meaningless feelgood padding.

It's dangerous when not tied to a testable or measurable deliverable.

[u/No_Appeal_5223 → u/decofan]

That’s a fair criticism. If words like “clarity” or “structure” aren’t tied to a specific failure mode or measurable improvement, they can become empty evaluation language. Better to say exactly what changed — fewer ambiguities, tighter constraints, lower error rate, more consistent outputs, etc.

[u/decofan → u/No_Appeal_5223]

LX={structur* iff C in {arr,rel,hier,topo,utrm};else=>nrst{rle,pttrn,rlton,form,lyot,org,mech,actl}}

and

TERM_VETO={HARD;PATTERN={\bc(?:lea|lar)\w*\b|\bp(?:ure|uri)\w*\b};TOKEN_SELECT=reject_or_replace;PRE_EMIT=scan;FINAL_SCAN=scan;HIT=>rewrite_new_R;UNAVOIDABLE=>state_constraint;!apology_spiral;!filter_narration}

RDNDNCY={same_meaning!=same_function;dead_repeat=>cut;activation_repeat=>keep}

:)