Why Writing Your Own Prompt Library Is a Competitive AdvantagePermalink

Most people treat ChatGPT prompts as disposable: they write one prompt, use it once, maybe copy-paste it from someone else, and move on. But the people who get the most value from AI don’t just use prompts. They build prompt libraries.

I’ve worked with clients who jump straight to enterprise AI tools or hire “prompt engineers” or “AI engineers” because they think building their own prompts is too technical or not worth the effort. Others copy-paste prompts from LinkedIn (often from hype-dealing influencers) without adapting them, assuming one size fits all. And many more just use ChatGPT ad-hoc, asking one-off questions, never building a systematic approach for how to think about prompting.

The problem with these approaches is that they’re missing the strategic value of owning their own prompt library. When you build and maintain your own prompts systematically, you’re not just getting better results, you’re creating individualized proprietary knowledge that becomes a part of your competitive edge.

I have my own extensive prompt library, refined by deeply evaluating themes across my work, which helps augment my own intelligence by systematizing common workflows, questions that I ask, and guidelines for what “high quality” AI outputs look like. It’s not just a collection of prompts—it’s a system that makes me more effective at my work.

As you build your own prompts, you create your own personal “prompt library.” This has many benefits, including the strategic advantages outlined below. But more fundamentally, it transforms how you work with AI: from reactive (writing prompts on the fly) to systematic (having proven approaches ready when you need them).

Benefit 1: Being a part of your own proprietary AI secret saucePermalink

As you experiment and create prompts, you create your own internal repository that becomes a part of your edge. These prompts become a part of how you get your results and are prompts that your competitors don’t have.

For my work, I find that I can systematically define how I, for example, prototype MVPs quickly, develop and ship certain features, do due diligence, and learn new topics quickly using AI, and all of those are key parts of my workflow now all amplified by AI.

Benefit 2: Gives you a set of prompts for different use casesPermalink

Some clients I’ve worked with have folders to organize their prompts, and then when they need to do a specific type of task, such as due diligence or analyzing reports, they go to that folder and they just copy and paste those same prompts into ChatGPT.

Benefit 3: Lets you build your own “test” to test against any new LLMsPermalink

New versions of ChatGPT and other LLMs come out all the time, and there’s so much hype and press around how these LLMs are now smarter than humans, curing diseases, automating away everyone’s jobs, and so on. AI companies all have their own internal prompts that they use to test any new LLM, and so should you. Having your own personal prompt library helps you get past the fluff and see when a new LLM actually does better for your specific use case.

How to organize your prompt libraryPermalink

Here are some tips for how to organize your prompt library:

• By use case: Create folders/categories for different types of tasks (e.g., “Content Creation”, “Data Analysis”, “Email Drafting”).
• By department: If you work across teams, organize by function (Marketing, Sales, Finance).
• Version control: Keep track of which version of a prompt works best. Note what changed between versions. Version control also exists natively in Google Docs (you can see the history of revisions) as well as in tools like Git (for the technically inclined).
• Metadata: For each prompt, document:
  • What it does
  • What inputs it needs
  • What outputs to expect
  • When to use it vs. alternatives
  • Known limitations or edge cases

Tools for organizing a prompt libraryPermalink

• Simple: Google Docs, Notion, or even a text file
• Advanced: Dedicated prompt management tools (though these might be overkill at the start)

Use your prompt library to test out new LLMsPermalink

When a new LLM version comes out (ChatGPT-5, Claude 4, etc.), don’t just read the press releases. Test it yourself:

1. Take 5-10 prompts from your library that represent your core use cases
2. Run them on the new LLM with the same inputs you’ve used before
3. Compare outputs side-by-side with previous versions
4. Document: Is it better? Worse? Different in ways that matter?

This gives you real, actionable data about whether the upgrade is worth it for your work, not just whether it’s better at math competitions. It also helps you separate hype from reality and to tune out AI snake oil salesmen and FOMO.

ConclusionPermalink

Building your own prompt library isn’t just about organizing your prompts, it’s about creating a systematic approach to working with AI that becomes a competitive advantage. When you own your prompts, you own your workflows. When you document what works, you build institutional knowledge. When you test new LLMs against your actual use cases, you make informed decisions instead of following hype.

The best part? You don’t need fancy tools or technical expertise to start. A simple Google Doc or Notion page with your prompts, organized by use case, is enough to begin. The key is starting systematically and building over time.

Most people will continue treating prompts as disposable, copying from others, or jumping to enterprise tools. But the ones who build their own prompt libraries will have something their competitors don’t: a systematic, proven approach to getting value from AI that’s tailored to their specific work.

Start small. Pick one use case, maybe email drafting, or data analysis, or content creation. Write a few prompts, test them, document what works, and iterate. Over time, you’ll have a library that becomes part of how you work, not just a tool you use occasionally.