Prompt engineering is like science. It pays to manage your prompts like code respositories and for good reason.
Write them once, and store them. If you do that like email, you ensure you can reproduce, scale and continously develop your capability.
Think of it like an asset you take with you everywhere.
Why managing your prompts is worth the effort
- You can reproduce things later:
- If you have prompts written out, you’ll be able to ‘redo’ work you did should you have to reproduce something.
- You can provide evidence as to how something was created:
- You may want to show someone else how to do something at a later time.
- Recording it, allows you to send your prompts to someone else for review and validation (and improvement)
- If you version control your prompts, you can view what’s changed:
- You can see how a model is behaving using a particular model - and make adjustments when models change.
- Re-usability:
- You never know when you might want to use a specific prompt again. Having them documented and on file is a great way to manage your prompts.
Where to store your prompts
You can store prompts anyway you like.
You might simply want to use google docs, with good naming conventions.
Even better you might want to use a free tool like Obsidian.
Or you can use a professional tool (free or Paid) like Notion.com.
You could even just use something like Apple Notes, or the Google Keep Notes App. All are valid - although having a way to organise and name them is pretty important.
You probably want to name them:
- Brief descriptor: summarise_research_biology_101
- Versioning: v.1.0.0, v.1.1.0, V.2.0.0 etc.
- Include the date or sprint: v.1.2.0-20250526 (where 2025 is the year, 05 is the month, 26 is the day)
Conclusion
Treating prompts as code, not throwaway text ensures your AI work is robust, auditable, and continously improving. If you spend some time curating it, over time you’ll turn a prompt library into a personal strategic asset that scales to your needs as you evolve.