How I Organize 200+ AI Prompts Without Losing My Mind
For about a year, my prompt library lived in Apple Notes. One long note called "AI stuff." Then a few sub-notes when that note got too long. Then a folder. Then several folders. Then a tagging system I invented one Sunday and stopped following by Wednesday.
The breaking point was around 50 prompts. Not because Notes can't handle 50 entries — it can handle thousands. The breaking point was that I could no longer *find* the prompt I knew I had written. I'd remember the gist, search for the wrong keyword, give up, and rewrite it from scratch. The library wasn't broken. My ability to retrieve from it was.
I now have somewhere north of 200 prompts I actively use. Image generation, video, marketing copy, product specs, customer research, code review, weekly planning. They live in a system I'd describe as "boring on purpose." Boring is the point. A prompt library only earns its keep when retrieval takes less effort than rewriting.
This is what I learned getting there.
The notes app stops working at 50
Three problems show up almost simultaneously when a flat notes app crosses about 50 prompts:
Search becomes unreliable. You search for "landing page" and get 12 results, none of which are the one you wrote. You wrote it as "homepage hero" three months ago.
You forget what you have. A new use case appears, you write a fresh prompt for it, and only later realize you wrote nearly the same thing in February. Now you have two prompts solving the same problem and no way to know which one is better.
Iteration disappears. You tweak a prompt that's working "okay," it gets a little better, then breaks. You can't roll back because the previous version is gone. So you avoid editing prompts that work, even when they could be much better.
Folders alone don't fix any of this. A tagging convention you don't enforce doesn't fix it either. What fixes it is treating each prompt like a small piece of software — with a clear name, a stated purpose, a category, and a version history.
The structure I actually use
There are five things I want to know about every prompt in my library, and the system has to surface all five within about three seconds.
- What does this prompt do — one-sentence purpose, not a clever name
- Where do I run it — Claude, ChatGPT, Midjourney, Sora, Suno, etc.
- What category — marketing, product, image, video, music, research, ops
- How well does it work — am I confident in this prompt or still tuning it
- Has it changed recently — what version is this and what changed
I store each prompt with title, purpose, target tool, category, status (draft / tested / favorite), and the prompt body itself. Edits create a new version rather than overwriting. Old versions stay accessible but collapsed.
That's it. There's nothing fancy. The reason it works is that every prompt in my library has all five fields filled in, every time. No exceptions.
A few real examples from my library
Here's one I use almost weekly when I'm writing landing page copy for a new feature:
You are reviewing a landing page section for a SaaS product aimed at solo founders and creators. The product is Super Prompts, a tool for organizing and running AI prompts. I'll paste a draft. Tell me: (1) the single weakest sentence and why, (2) the strongest sentence and why, (3) one specific change that would make the section convert better. Be direct. No hedging. No "consider doing X" — say what to do.
Category: marketing. Tool: Claude. Status: favorite. I've revised this prompt four times. The current version is the one that stopped giving me useless "consider varying sentence length" advice and started telling me which sentence to cut.
Here's a research prompt I run before every product decision:
I'm about to make a product decision. Before I commit, I want a quick stress test. The decision is: [DECISION]. The reasoning behind it is: [REASONING]. Play three roles in sequence: (1) a pragmatic engineer worried about implementation cost, (2) a skeptical user who hates change, (3) a future me reading this in six months. Each gives me one sharp objection. Then tell me which objection is most likely to actually matter.
Category: product. Tool: Claude. Status: favorite. I keep it generic with bracket placeholders so it works for any decision. The three-role structure keeps the model from being agreeable.
And here's an image prompt template I reuse for product mockups, blog headers, and social posts:
Cinematic product photography. [SUBJECT] on a [SURFACE], [LIGHTING], shallow depth of field, soft shadows, [COLOR PALETTE]. Shot on a 50mm lens. No text, no logos, no people. High detail, photographic realism, 4k.
Category: image. Tool: Midjourney. Status: tested. The placeholders are the only things I change. The rest is the part that took me 30 generations to dial in.
These three are not exceptional. They're representative. Every prompt in my library has roughly this structure: a clear job, a target tool, a status I trust, and a body that I've actually tested.
Versioning is the boring superpower
The thing I underestimated when I started organizing prompts was how much value compounds from versioning.
A prompt that works "okay" today is not the same prompt that will work next quarter. The model will change. Your product will change. Your taste will change. Without versions, every edit is a coin flip — you might improve it, you might make it worse, and you'll have no way to know which until next time you use it.
With versions, edits become cheap. I can change one line, run both versions on the same input, see which one I prefer, keep the winner. The losing version doesn't disappear — it stays in the version history in case I want to revert later. Three or four iterations like this and a "decent" prompt becomes a great one.
Most of my favorite prompts in the library are at version 4 or 5. None of them were great at version 1. The versioning is what let them get there.
Status labels stop you from trusting bad prompts
Every prompt has one of three statuses:
- Draft — written, not yet tested in real work. Don't trust the output.
- Tested — used at least three times, output is reliable. Safe to use.
- Favorite — used regularly, output is consistently strong. Reach for these first.
This three-level distinction matters more than it sounds. Without it, you start treating every prompt as equally trustworthy. You run a draft prompt on a real project, the output is mediocre, and you can't tell whether the prompt is bad or the model is having an off day. With status labels, you know — and you also know which prompts are due for promotion or demotion.
What I'd do if I were starting today
If you have fewer than 20 prompts, a notes app is fine. Don't overbuild.
Past 20, start adding the five fields manually — even if your tool doesn't support structured data, write them at the top of each prompt. The discipline of filling them in matters more than the tool.
Past 50, move to something with structured fields, search across fields, and version history. Whether that's Notion, a Google Sheet with an opinionated schema, an Airtable base, or a dedicated tool like Super Prompts — pick one and commit to it. The retrieval problem is what kills your library, and only structured retrieval fixes it.
Past 100, stop adding new prompts when you don't need to. Half the prompts in a 200-prompt library are doing the work. The other half are bloat you forgot to delete. Audit quarterly. Demote drafts that never got used. Delete duplicates. The library should compress, not just grow.
The prompts you write are an asset. Treat them like one.