Should companies create internal ChatGPT prompt libraries or let employees figure it out themselves?

You’ve probably seen this movie before:

Company announces a bold new AI initiative. Everyone gets access to ChatGPT Enterprise or some shiny new tool. There's a slick internal landing page. Maybe even a “Prompt of the Week” Slack channel.

And then… nothing.

Okay, not nothing. A few power users go wild with it. Somebody in marketing uses ChatGPT to draft five variations of the same launch tweet. HR rewrites vacation policy in the tone of Oprah. One enterprising sales rep builds a prompt for summarizing customer feedback calls.

But ask around a month later, and the story is eerily familiar: “Yeah, I opened it a couple times. Not sure what to do with it.”

Welcome to the AI treadmill: full of optimism up front, abandoned in the corner of the org chart like a $2,000 elliptical doubling as a coat rack.

Prompt libraries: solution or side quest?

So here’s the hot debate inside forward-looking companies right now: Should you build and share an internal prompt library to help your people use AI more effectively? Or just let them figure it out?

Because one sounds structured and safe—share knowledge, reduce duplication, scale good practices. And the other sounds empowering—encourage exploration, maximize creativity, trust your team.

Some version of this argument is happening in almost every AI leadership meeting right now. But let’s cut through the theoretical framing and look at what’s actually going on.

Because neither approach works if you ignore the root problem.

Good prompts don’t live in vacuum-sealed Google Docs

The idea of a prompt library is seductive. It feels like a neat solution: compile a list of approved, high-performing prompts and share them across teams. Job done.

Except that’s not how prompting works.

A great prompt is rarely transferrable straight out of context. What sings in finance might fall flat in marketing. A prompt that saved someone five hours writing legal summaries might blow up in sales if the tone’s even slightly off.

These aren’t generic macros. They’re little acts of reasoning. Specific to the person, the data, the problem, and even the phrasing.

It’s like trying to share a stand-up routine across countries. The joke structure might survive. The actual laughs? Not so much.

One senior PM told me their team made a hearty attempt at a shared prompt doc. “It was fine for onboarding,” he said. “But by Week 6, it had collapsed under the weight of 200 janky entries with no versioning or comments.”

Basically: digital junk drawer.

And DIY isn’t it either

The Romantic idea of letting everyone “figure it out themselves” isn’t as noble as it sounds.

In reality, most employees won’t google their way into becoming elite prompt engineers in between status meetings and Asana updates.

They’ll try once or twice, get mediocre results, and quietly assume the AI is just overrated. Or they’ll copy-paste something from a Twitter thread like “Act as a world-class strategist,” get plausible-sounding fluff, and waste an afternoon dressing it up.

Meanwhile, across the company, ten different people are crafting slightly different prompts to solve the same problem—rewriting emails, summarizing meetings, cleaning up messy copy. Not innovation. Duplication.

And eventually? Ambivalence.

If you think AI adoption is slow because people are resisting change, you’re looking in the wrong place. They’re not resisting.

They’re just underwhelmed.

The real unlock isn’t libraries versus freedom. It’s friction.

Let’s be honest: prompting is a weird muscle.

It’s not writing. It’s not coding. It’s somewhere between strategic thinking and controlled curiosity. Done well, it feels like coaching a very smart, very literal intern who knows everything but lacks judgment.

Most people haven’t built that muscle yet.

So asking folks to swim on their own—or giving them an outdated recipe book—isn’t helping.

The companies actually getting value from AI right now? They’re not religious about prompt governance. They’re obsessed with embedding prompting into actual workflows.

They make it stupidly easy to get tangible results on real work.

Three examples:

• A claims processing firm rebuilt their review workflow with built-in AI prompts. What used to take 3 hours now takes 40 minutes. No “prompt library”—just well-placed microtools embedded in the app they already used.

• Klarna trained employees not just on “good prompts” but when and how to recognize AI-shaped problems. Result: 80% of the company reportedly uses ChatGPT weekly, not for philosophical inquiries, but for real, quantifiable tasks.

• One company’s finance team created a living Notion workspace with reinvention-friendly templates: “Q&A → Budget summaries,” “Customer sentiment parser,” “Reconciliation explainer.” Think code snippets, with usage notes. The kicker? It’s commentable and versioned. A GitHub for prompts.

In all these cases, the emphasis wasn't, “Here’s a prompt.” It was, “Here’s how you speed up this specific painful task—start here, then tune.”

Prompting isn’t typing. It’s thinking.

Let’s stop pretending prompts are reusable plug-ins.

They’re more like mini strategies—short strings of logic that encode how someone thought about the problem.

Which means that real AI literacy isn’t about having the right prompt. It’s knowing how to spot weak ones. How to iterate. How to reverse-engineer a model’s misunderstanding. How to identify whether the output is hallucination or haze.

The best AI users sound more like product managers running A/B tests than employees filling in templates.

They ask:

• What exactly am I optimizing for?
• How should I structure this reasoning?
• Where is the model likely to misinterpret nuance?

That’s not clerical. That’s strategic.

So yes, a shared prompt can help kick off a task. But the true muscle is in the adaptation. That's what an internal prompt system should encourage.

So what should companies do instead?

Here’s where it gets interesting.

The forward-motion companies we've talked to? They’re building what we’ll call “prompt ecosystems,” not “prompt lists.”

Here’s what that looks like:

• Seed vs. Stagnate: Start with a few high-impact prompts for common needs (summarizing, extracting, rewriting) tied to actual tasks. But don’t freeze them in a wiki. Treat them like mutable templates, with notes and context.

• Live iteration: Create channels where people can workshop prompts live. Prompt battles. Slack threads. Brown-bag sessions. Let people see what’s really working—and failing.

• Feedback loops: Track performance. If a prompt led to a better sales email click rate or shaved time off a report, log that. If it fizzled, talk about why.

• Ownership, not centralization: Let teams manage and evolve their own prompt systems. Finance should track what works for finance. Marketing has its own flavor. Cross-pollinate the learnings—but don’t force-fit.

• Cultural shift: Build psychological safety. It should be totally normal for someone to say, “I tried a wild AI idea and it flopped.” That’s how the real creativity shows up two iterations later.

You don’t scale magic through permission. You scale it through context, curiosity, and community.

Standardization vs. creativity isn’t a zero-sum game

Here's the nuance: You need guardrails. Just not straitjackets.

The best internal prompt systems are part Figma template, part Stack Overflow thread, part improv class. They start structured, then evolve. They reward transparency over polish.

Think of them not as libraries, but as prompt gyms.

You don’t show up to get a script. You show up to learn how to think better, faster—with the help of others who are trying to do the same.

Because ultimately, the companies who win at this aren’t the ones hoarding the cleverest prompts.

They’re the ones building a workforce that understands how to think in partnership with a powerful, flawed, occasionally poetic machine.

Final thought: don’t let the tools set your goals

Too many companies treat AI like a solution looking for problems. They start with the new capability—AI!!—and then try to reverse-engineer value.

Flip it.

Start with: What are the top 3 workflows in your org that are annoying, repetitive, or bottlenecked by human time?

Then ask: Can AI help here—by writing better, parsing faster, or freeing up minds for higher-order thinking?

If the answer is yes, that’s when you write the prompts.

Not from a committee. Not as an artifact. But as something living, useful, and shared.

Because prompting isn’t automation. It’s a new form of asking better questions.

Teach people that, and you won't need to worry about libraries at all. They’ll build what they need—together.