AI Prompt Library, Which Ones and What to Store

There's a gap on the first page for ai prompt library that took me about ten minutes with the GitHub API to confirm, and once you see it you can't unsee it.

The public collections that rank are not the ones anybody uses. One of the results on page one is a GitHub repository with 121 stars as I write this. Meanwhile the best-known prompt collection on GitHub, the one that used to be called Awesome ChatGPT Prompts, sits at over 166,000 stars and doesn't appear on that page at all. A university library guide ranks. A note-taking product's landing page ranks. The actual centre of gravity of this subject is somewhere else entirely.

So the short answer is that the ranking libraries are a poor guide to the good ones, and more importantly that a library is only as useful as what each entry records. A finished prompt with no note attached rots in about six months. Below is the adoption data, then the record schema I'd actually build, which for image and video work needs fields a text library never thinks about.

Adoption Versus Ranking

All star counts pulled from the GitHub API today, so they'll have drifted slightly by the time you read this.

I'm not making a point about search being broken. Repositories rank badly for commercial-ish queries and that's normal. The point is practical, which is that if you're picking a library by opening the first page of results, you're picking from a set that has almost no relationship to what the people doing this work actually reference.

The one non-repository I'd send people to is Wharton's, which ranks first and deserves to. Their prompt library is built by Ethan Mollick and Lilach Mollick, it's free, it's licensed Creative Commons Attribution 4.0 so you can adapt and use it commercially with credit, and it lives in a Notion database. Each entry carries clear instructions, suggested use cases, and model options, which is more structure than most collections bother with.

They also do something I wish more collections did, which is warn you that outputs may not be correct and recommend testing before you reuse or share anything. That's an unusual amount of honesty for a resource that could just as easily present itself as authoritative.

Almost All Of Them Have The Same Blind Spot

Text. It's all text.

Scroll the big collections and you'll find role prompts, writing prompts, coding prompts, analysis prompts, interview prompts. What you won't find much of is image or video prompting, and the one image-focused repository in my table above, the GPT-4o images one at eight thousand stars, is an outlier rather than a category.

That's odd given where the search volume sits, and I think the reason is structural rather than anything to do with interest. A text prompt is portable. You can paste it into any model and roughly the same thing happens. An image prompt is bound to a model, a version, an aspect ratio, and often a seed, and the moment any of those change the prompt means something different. So collecting them as bare strings is close to useless, which is why nobody does it well.

Which points straight at what a real image library has to store.

The Record Schema

Here's the thing I'd build, and the middle column is the part that separates a useful library from a folder of strings.

The field I'd fight for is the fourth one from the bottom, the style block stored exactly as typed. Rewording it slightly is enough to drift a result, and "warm side light" versus "warm light from the side" is a real difference in outcome even though it's the same sentence in English. If your library normalises whitespace or reflows text, it's quietly corrupting your entries.

The one people skip is "what you changed to get here". A prompt tells you where somebody landed. The note tells you how they got there, and six months on the model will have moved and the prompt might not work while the lesson still will.

An Entry, Written Out

Abstract schemas are easy to nod at, so here's one filled in.

Goal. Empty establishing shot for a book trailer, meant to be animated afterwards. Prompt. A narrow city street at dawn, wet asphalt, nobody in frame. A single shop sign glowing at the far end, everything else in blue pre-sunrise light. Locked-off wide shot, low camera height, 35mm. Muted colour, fine grain. 16:9. No text, no vehicles. Model and date. Local run, this summer. What changed to get here. Removed the two figures walking. Emptying the frame is what made it animatable, because nothing left in it can move wrong. Aspect. 16:9, set before writing, since it recomposes rather than crops. Keep rate. Two of three. Still works? Yes, checked recently.

Seven lines, maybe ninety seconds to write down. The value isn't the prompt, it's line four, and line four is the thing no public library will ever contain for you because it's about your subject and your failure.

More worked examples in that format are in ai image prompt examples.

Entries Expire, So Give Them A Shelf Life

The part nobody builds. Every library I've seen, mine included for a long time, is append-only, which means it fills up with entries that stopped being true and you lose the ability to trust any of it.

That third row is the sneaky one. Prompts that carry instructions for problems the model used to have still work, technically, and every line of that dead scar tissue is spending your attention and the model's for nothing. Worth rereading your saved ones once a year specifically hunting for it.

My own approach is embarrassingly low-tech. Plain text files in the same repository where the rest of my projects live, one per domain, dated headings. I built a whole privacy-first desktop app for organising my life and I still keep prompts in a text file, which probably says something about how much tooling this problem actually needs.

The Best Free Library Isn't Called One

Something I only noticed while pulling those star counts. The highest-quality collection of image prompts I know of isn't listed as a prompt library anywhere, because it lives inside vendor documentation.

Google's image generation guide ships thirteen named templates, each one a fill-in-the-blank skeleton with the slots marked in brackets. Photorealistic scenes, stylised illustrations and stickers, accurate text inside an image, product mockups, minimalist compositions with negative space, sequential comic panels, style transfer, adding and removing elements, inpainting through semantic masking, combining multiple images, detail preservation across an edit, turning a sketch into a finished image, and grounding a generation in live search results.

Thirteen templates covering most of what anyone actually makes, written by the people who trained the model, free, and structured as templates rather than finished prompts, which is the format that survives being copied.

Seed your library with those and you've skipped a month of collecting. Then the entries you add on top are the ones carrying your subject and your notes, which is the only part a public collection could never have supplied.

I'd add one caution, since it's a vendor teaching their own product. The slot names are more portable than the wording. "Shot type, subject, setting, lighting, camera angle or lens" is a checklist that works on any image model. The precise sentence structure around it is tuned for theirs.

Should You Pay For One

For text, I wouldn't. Between the free collections above, the vendor documentation, and Wharton's, everything a paid pack sells is already sitting in public, and you'd be buying finished prompts, which are the least transferable thing in this whole subject.

For images the argument is slightly better and it isn't about the prompts. It's about vocabulary. Scrolling a few hundred image prompts with their outputs attached genuinely teaches you terms you didn't have, and that's real learning even when it's unstructured. Free collections do this too, so I'd still start there.

The version I'd actually pay for doesn't seem to exist, which is a library where every entry has the failed version next to the working one. That's the information, and it's the thing collections systematically strip out because failures don't look impressive in a gallery.

Questions

How many entries should mine have? Small. I keep about fifteen and I use maybe five, which matches what most people find once they stop collecting for the sake of it. Anyone with four hundred saved prompts has an archive rather than a library.

Where should I store them? Anywhere you'll actually reopen. Not chat history, which is where I lost a good one permanently by scrolling past it. Standing constraints belong in a custom instructions field if your tool has one, since those apply to everything.

Do prompt library products add anything over a text file? Version history and search, mostly. For a set this small, a file is enough. The templating side, which is the genuinely useful pattern, is in chat gpt prompts.

Is Wharton's library good for image work? It's aimed at text and teaching, and that's what it's good at. For pictures you're better off with vendor documentation and your own notes.

What if I want ideas rather than a library? Different problem, different fix. Idea generation is in ai prompt ideas.

Do star counts mean quality? No, and I'd rather not imply that. They measure attention, which is a different thing. I used them because they're one of the few numbers in this subject that anyone can verify in a browser.

What I'd Do This Week

Open whatever file or app holds your saved prompts and add one line to each entry. What you changed to get there.

You'll find two or three you can't answer, which means you copied them and never learned anything from them, and those are the ones to delete. Then add a date. That's the whole upgrade, and it turns a pile of strings into something with a shelf life. The framework underneath all of it is in chatgpt prompts.