Your AI Prompts Are Blind

The prompt doc everyone has and nobody uses

Somewhere in your company there's a document titled something like "AI Prompts for Support." It has a dozen carefully worded blocks. "Summarize this angry customer email and draft an empathetic reply." "Explain our refund policy in a friendly tone." Someone spent a weekend collecting them from Twitter threads and prompt-library sites.

And nobody uses it. Because using it looks like this: copy the prompt, open ChatGPT in another tab, paste the prompt, then paste the ticket, then realize the model doesn't know the order number so paste that too, then the shipping status, then correct the refund policy it invented, then the ship date it hallucinated, then finally get an answer you could have written yourself in the time it took to feed it. It felt like the future for about a week. Then it became more work than just replying.

Here's the uncomfortable diagnosis: the prompts aren't the problem. They're fine. The problem is that they're blind.

A support answer is 10% phrasing and 90% facts

Think about what a good support reply actually contains. A sentence or two of acknowledgment, and then a pile of specifics: your order shipped Tuesday, here's the tracking, it's held at customs, you qualify for a refund under clause two, here's the timeline. The phrasing is maybe ten percent of the value. The other ninety percent is facts about this specific customer and this specific order.

A copy-paste prompt has access to exactly zero percent of those facts. It knows how to write. It doesn't know anything. So you become the model's data layer, the human who fetches the order, looks up the policy, checks the tracking, and pastes it all in. You've automated the easy ten percent and kept the tedious ninety for yourself.

That's the trade nobody mentions when they hand you a prompt library. The prompt was never starving for better wording. It was starving for context.

The same prompt, blind and wired

Here's the same request, written two ways.

Blind, the way it lives in a prompt doc:

Summarize this ticket and tell me what to do next.

Fine words. No eyes. It can only summarize whatever text you remember to paste, and it'll guess at everything you forget.

Now here's how we write the same thing in Auxx, where we call it a skill: a reusable prompt wired to the data it needs. This is the actual Summarize Ticket skill we ship:

Context
You are a support operations analyst reviewing a customer support ticket. Use
get_thread_detail for the conversation, list_notes for internal notes, and
get_entity_history for the timeline of edits. The ticket may also have a linked
contact and other records (orders, deals).

Task
Create a ticket summary: the issue in 1-2 sentences, a timeline of key events
(max 5 bullets), actions taken so far, current status (resolved, waiting on
customer, waiting on internal team, or unresolved), and the single most important
next step.

Constraints
Be concise and actionable. If information is missing, write "Unknown." On
conflicting signals, prioritize the most recent information. If there's no ticket
in context, ask which ticket to summarize first.

Read the Context block. Those get_thread_detail, list_notes, and get_entity_history lines aren't decoration. In the app they're live hooks that go and fetch the real thread, the real internal notes, the real edit history before the model writes a word. The task is the same instruction as the blind version. The difference is the model now has eyes.

The three parts that make a prompt see

Every skill we ship has the same three-part shape, and each part is doing a specific job.

Context is where the prompt goes and gets the facts. This is the part a copy-paste prompt structurally cannot have, no matter how well it's worded. It names the live sources (the thread, the notes, the order, the knowledge base) and fetches them. It's the ninety percent.

Task is the exact shape of the output. Not "summarize this" but "give me the issue, then a timeline of at most five bullets, then the status from this specific list." A fixed shape means every run is comparable, and reps stop getting a paragraph one time and a bulleted list the next.

Constraints are the guardrails that make it trustworthy. Write "Unknown" instead of guessing. Prefer the most recent information when signals conflict. Ask first if there's no ticket in context. These lines are what kill the confident-hallucination failure mode: the model saying something wrong in a way that looks right. A blind prompt has no constraints because it has nothing to constrain against.

Why this compounds into a moat

Here's the part that matters if you're building a support operation rather than just clearing today's queue.

A wired skill is reusable and connected. Your best rep's judgment (which order to check, what to never promise, how to structure the hard reply) gets written down once, as a skill. It pulls the same facts every time. The whole team runs it. And it gets better as your data gets better: cleaner order records, a sharper knowledge base, better notes all flow straight into every answer the skill produces.

A prompt in a browser tab can do none of that. It's frozen the moment you paste it, it fetches nothing, and it improves only when someone remembers to hand-edit the doc. One of these is an asset that appreciates. The other is a snippet that rots.

Stop collecting prompts

Stop collecting prompts. Start wiring them.

The measure of an AI support prompt isn't how clever it reads. Anyone can write clever; that's the easy ten percent. The measure is how much of your actual data it can reach before it answers. A blind prompt makes you do the ninety percent by hand. A wired skill does it for you.

If you want to see wired skills doing real work, we broke down four of them for Shopify teams in four AI skills every Shopify support team should steal. And when you're ready to build your own, here's how to write one that pulls its weight.