AXHUB LECTURE · INTRO L2

How to put AI to work
— like briefing a new hire

Time about 30 minutesWhat you need one chatbot + one piece of real work materialFor anyone who tried it but got flat results

You don't need to know "prompt engineering." If you've ever briefed a new hire on a task, you already have the instinct you need. This lecture moves that instinct into five habits.

1Don't hand over the whole thing

You don't tell a new hire "just go write the report."

Same with AI. Throw the whole thing at it and you get a flat result.

Break the task into steps and the result changes. Real-user reviews reach the same conclusion — short step-by-step instructions beat one grand request.

Practice 1 — break it down
Instead of "write last month's sales report" —

(1) In this Excel, find only the items that changed 20% or more from the previous month
(2) Organize the found items into a table with a guessed cause
(3) Summarize in 3 lines for reporting to the manager

Pick one of your tasks, write it out as (1)(2)(3), and run it in order.

Check: did you verify each step's result by eye before moving to the next?

2Give the context first

You don't put a new hire to work without introducing the company.

Before the request, tell it three things — who we are, who will read this, and what tone it should have.

The same request produces different sentences once the reader is decided.

Practice 2 — compare before and after adding context
Instead of "write a notice" —

We're a neighborhood Pilates studio. The reader is a member who hasn't come in 3+ months.
In a no-pressure tone, write a 4-sentence notice about a re-enrollment discount.

Run both versions and compare the results — you'll feel the difference in your bones.

Check: does the instruction include both "to whom" and "what tone"?

3Tell it what a good result is

A new hire doesn't know the standard in your head. Neither does the AI.

Showing one good example is faster than ten lines of explanation.

Practice 3 — show the standard
Below is a reply I think I wrote well last month. Take this tone and length as the standard.
[paste the good example]

Now handle the item below by this standard.
[paste the new material]

Check: did you show "an example of a good result" at least once?

4Always review the result

You don't send a new hire's first report straight to your boss.

A person sees the AI's output at the end too. Numbers, names, dates, and links especially — always check them against the original.

This isn't inefficiency — a structure where a person reviews only the exceptions outperformed approving every one by more than double (analysis of 51 cases).

Practice 4 — build the review habit
From the result just now, pick out only the sentences with numbers, names, or dates and show them as a list.
I'll check them against the original.

Having the AI pull the checklist speeds up your review.

Check: among what you sent out today, is there a number that went out unchecked?

5Keep what you taught

Training a new hire leaves behind training material.

Stack the instructions that worked, the requests that failed, and your tone standard in one place.

This accumulation stays even if you switch tools. 77% of the hardest part that decided success or failure was operations like this, not the model.

Practice 5 — your own instruction drawer
One page in a notes app: "instruction drawer"
Format — task name / full instruction text / cautions

Check: did the instruction you made today go into the drawer?

When it doesn't work

The longer the instruction, the worse the resultIt's like handing a new hire a three-page policy — they can't do anything. Cut it to 3 rules + 1 thing to avoid + 1 line on tone, and break the rest into steps.
I ask to fix one spot and another changes"Rewrite it" is the problem. Name the scope of the fix, like "just sentence 2. Leave the rest as-is."
I gave an example and it copied the content tooIf the sentences from the example you gave as a standard show up in the result, add one line: "Use the example only for tone and format; take the content only from the new material."

Next lecture → L3. A shop owner's first AI — start with review replies · Full contents

Sources: Stanford Enterprise AI Playbook (review-exceptions-only +71% vs approve-each +30% · 77% of the hardest part is beyond technology) · real-user reviews (step-by-step instruction is more efficient) · "AI is close to a new hire" (solo-operator review). Full sources are in the axhub.net case library. The practice prompts are example lines to follow along.