How do you guys organize your prompts?

[yanyuz]

My prompt collection is getting a bit messy. I currently just save them in a massive Notepad text file, but it's getting hard to find specific styles I liked from months ago.

Do you use Notion, Obsidian, or just a spreadsheet?

I'm looking for a better way to tag and store my favorite prompts. Let me know your workflow!

 

[Aiwebmastery]

I’ve found that a plain collection of prompts becomes difficult to maintain once you have more than a few dozen.

I prefer organizing prompts more like reusable components:

- Give each prompt a clear name based on its purpose.
- Add tags for the task, model, language, and use case.
- Keep variables separate from the actual prompt structure.
- Store different versions instead of overwriting prompts that already work.
- Keep a few example inputs and expected outputs with each prompt.
- For prompts used in agents or workflows, I also keep track of where the prompt is used.

The versioning part has been especially useful. A prompt that works well for one model or task doesn't necessarily produce the same results after changing the model, so keeping the history makes it much easier to compare.

Notion or Obsidian can work well for this, but I think the organization system matters more than the specific tool.

 

[MariaLi]

Since finding an old style is the main problem, I’d save a thumbnail of the result next to the exact prompt and settings. A visual index can be easier to browse than a list of prompt names. Add a short “what I liked” note, such as lighting, composition, or texture.

I’d migrate only the favourites you still use first, rather than reorganising the entire Notepad file. Keep experiments in a separate inbox and promote a prompt to the favourites collection only when you have a result worth keeping.

 

[Aiwebmastery]

That distinction is very close to how I’ve been approaching this in AIWebMastery.


For me, reproducibility is less about getting identical wording and more about being able to reproduce the same decision context and verify that it still passes the same checks.


I’ve found it useful to keep the model, prompt/version, tool inputs, captured tool results, decision, and outcome together. Then a replay can run in read-only mode against the captured context without automatically repeating side effects.


Write actions are treated separately and need an explicit approval before they are executed again. That also makes the audit trail much more useful: you can see what the agent observed, what it decided, what actually happened, and what was learned from the outcome.


So I’d say my target is primarily reproducible decisions + reproducible checks, rather than identical generated text. The wording can legitimately change when the model or context changes, while the decision should remain explainable against the same evidence.