Your Team Has AI. Nobody Taught Them How to Use It.

Why generic libraries fail

• Generic prompt libraries don't get used, because the examples aren't anyone's job. People open them once, don't see their own work, and never come back.
• We build yours from real tasks pulled out of real weeks — the five things each role does every week, in their words.
• Every entry gets tested before it goes in. That's the step everyone skips, and the reason most libraries rot inside a year.

“If nobody recognizes their own job in the library, you built a document, not a tool.”

How it works

• Most AI spend is the wrong model doing easy work. Moving a workload one tier down cuts its cost 60%. Two tiers, 80%.
• Nothing about the work changes. Only the routing does.
• We benchmark on your actual tasks, not a public leaderboard — then write the default per role, so it isn't left to each person's habit.
• Savings depend on your current mix. A team already defaulting to the mid tier won't see 60%. The assessment tells you your number first.

“Nobody routes, because nobody told them to.”

What we build

• Every session starts from zero. The fix isn't better prompting — it's a file. Project instructions that load automatically, so nobody re-explains the basics.
• But the moment you build that file, you inherit a second problem: it loads into every session, forever, and it grows. So we set a size budget and a health check that measures the right thing.
• A handoff template per project — current state, goal, key files, what works, what's open. It works whether the next session is a colleague or the same person after a reset.
• And one rule that prevents most of the rot: each fact gets exactly one home.

“A context file that grows unchecked is a file that makes every session worse, forever.”

What the catalog enforces

• A required shape. Name, description, owner, what it's for, what it's not for, last-reviewed date, worked example. No metadata, no entry.
• Rating plus usage. Usage is the honest signal — what people actually run beats what people say is good. Search sorts on a blend, so the good one wins.
• Visible trust tiers — Golden, Community, Experimental, Archived — with a promotion path and a named reviewer.
• An expiry date on everything. Sprawl is what happens when nothing is allowed to die.

“Sprawl isn't too many skills. It's too many skills of unknown quality.”

What's in it

• Data classification, approved tools, and a plain-language list of what never goes into a prompt.
• Written to be followed — short enough to read, specific enough to act on. Not a fifty-page document nobody opens twice.
• The part that makes ours different: where the policy says “must” and no technical control exists, we say so, in the document.
• We write security policy for a living. The AI one is the same discipline applied to a newer problem.

“A policy that pretends to be enforced is worse than one that admits what it can't reach.”

What the inventory finds

• Which tools are in use, by which teams, on what kind of data — including the accounts nobody approved and nobody is paying for.
• Personal accounts holding work product. This is the one that surprises people, and it's the one with real exposure attached.
• Where consolidation onto approved tooling is easy, and where it will meet resistance because the unapproved tool is genuinely better.
• It is usually uncomfortable. It is always the most useful page in the readout.

“The only open question is whether it's happening on tools you can see, under rules you wrote.”

The numbers

• 40% of desk workers received AI “workslop” in the past month — content that looks plausibly polished but offloads the real thinking onto whoever receives it.
• Two hours to resolve each incident, on average.
• $186 per month, per affected employee — an invisible tax that never appears on an invoice.
• The researchers named the pattern, which is half the battle. You can't govern a failure mode nobody has a word for.

“The cost of untrained AI isn't the license. It's the rework it pushes onto everyone downstream.”

The arithmetic

• The mid tier runs at 40% of top-tier cost. The small tier at 20%.
• On summarizing, reformatting, extraction and classification, output quality is indistinguishable between tiers. That's most of the volume.
• This is why the 40–60% headline is conservative. It isn't our estimate — it's division.
• We quote ratios rather than prices deliberately. Vendors re-price; the shape of the ladder holds.

“Nothing about the work changed. Only the routing did.”

What the experiment found

• Consultants were randomized into three arms: no AI, AI, and AI plus a prompt-engineering overview.
• On a task chosen to sit outside the model's capability, the control group was correct 84.5% of the time. The AI groups dropped — untrained by 13 points, trained by 24.
• Their wrong answers were also better written — recommendation quality rose 25.1% for the trained arm. Polished, confident, wrong.
• The mechanism is named in the paper: the training increased copy-and-paste. More skill, more trust, less checking.

“This is why verification has to be taught alongside prompting — not after it, and not optionally.”

Training works — inside the boundary

• On work within the model's competence, the trained arm led on every measure: +42.5% quality versus +38% for untrained AI users.
• Task completion: 93% trained, 91% untrained, 82% control.
• The paper's own conclusion: the gap between trained and untrained was statistically significant across every specification, which “underscores the importance of the added overview.”
• So the training effect is real. It just isn't a free pass — which is the entire argument for pairing it with governance.

“Training is necessary. On its own, it is not sufficient — and the gap is where the damage happens.”