Prompts that work, written two ways

Twelve everyday business jobs, each asked the way a colleague would and briefed the way an engineer would. Copy either. Learn why the second one is the start of an agent.

Prompt library

The same job, asked two ways

Every prompt below is written twice. Once the way a busy person would ask a trusted colleague, and once the way an engineer briefs an agent. Both get the job done. The second one gets it done the same way every time, which is what you need before anything runs on its own.

Register one

Ask it like a colleague

Plain English, in your own words, with the facts the colleague would need. Good for one-off work in a chat window. The quality depends on how well you explain the job, and it varies from day to day.

Register two

Brief it like an engineer

Role, inputs, rules, output, quality check. The same shape every time, so the result is the same shape every time. This is how a prompt becomes a skill an agent can run on a schedule, with a person approving what matters.

Prepare for a first meeting with a prospect

Sales
Ask it like a colleague
I'm meeting [company] on [date]. Pull together what I should know before I walk in: what they do, how big they are, anything in the news, who I'm meeting and what they care about, and three questions worth asking. Keep it to one page.
Brief it like an engineer
ROLE: sales research analyst for a UK B2B supplier.
INPUTS: company name, website, meeting date, attendee names and titles, our last three interactions (CRM notes attached).
RULES: public sources only; cite each fact with its source and date; mark anything unverified as such; no guesses about revenue or headcount without a source.
OUTPUT: markdown, max 350 words: 1) company in two lines, 2) recent signals (hires, funding, news, tenders), 3) the people, 4) three discovery questions tied to the signals, 5) one risk to the deal.
QUALITY CHECK: every claim has a source; no claim about the prospect's internal plans.
On Adam AI: Sales analyst agent, from your CRM and public sources.Data: Fine for public AI if you remove your CRM notes. On Adam AI the notes stay in.

Follow up after a quote has gone quiet

Sales
Ask it like a colleague
We sent [customer] a quote for [what] on [date] and have heard nothing. Draft a short, friendly follow-up from me that gives them an easy way to say yes, no or not yet, and offers one useful thing rather than just chasing.
Brief it like an engineer
ROLE: account manager writing in plain British English.
INPUTS: quote summary, date sent, previous thread, our relationship history, one relevant insight we can share (attached).
RULES: under 120 words; one question only; no discounting; no false urgency; sign-off with the sender's own name.
OUTPUT: subject line plus email body, then a one-line note on when to send and what to do if there is still no reply.
QUALITY CHECK: reads as a human wrote it; the insight is genuinely useful without the sale.
On Adam AI: Sales agent, drafting for a person to send.Data: Never paste a customer’s quote into a public tool; prices and names are confidential.

Chase overdue invoices without upsetting good customers

Finance
Ask it like a colleague
Here is our aged debt list. Group the overdue invoices by how overdue they are and how good the customer is, and write me the right message for each group: a gentle nudge for the good ones, firmer for the rest. Flag anyone I should ring instead of email.
Brief it like an engineer
ROLE: credit controller.
INPUTS: aged receivables export (customer, invoice, amount, days overdue, payment history score), a list of customers in dispute or on a plan.
RULES: exclude disputed invoices; never threaten legal action in a first message; keep each message under 100 words; tone matches the band (1 to 14 days friendly, 15 to 45 firm, 45 plus formal).
OUTPUT: a table of customer, band, recommended channel, draft message; then the five accounts to phone and why.
QUALITY CHECK: amounts and invoice numbers match the export exactly; no customer appears twice.
On Adam AI: Finance agent with the accounts package connected; a person approves before anything is sent.Data: Aged debt is personal and commercial data. Do not upload it to a public tool.

Explain the month’s numbers to the leadership team

Finance
Ask it like a colleague
Here is this month's P&L against budget and last year. Tell me the story in plain English: what moved, why it probably moved, and the three things leadership should ask about. No jargon.
Brief it like an engineer
ROLE: finance business partner.
INPUTS: P&L actual vs budget vs prior year by line, headcount, one-off items list, notes from the previous month.
RULES: variances over 5 percent or over a stated threshold only; separate timing from real change; never invent a cause, say "to confirm with" and name the owner.
OUTPUT: 1) three-sentence summary, 2) table of material variances with likely cause and confidence (high, medium, low), 3) three questions for the meeting, 4) what to watch next month.
QUALITY CHECK: totals reconcile to the source; every cause marked with its confidence.
On Adam AI: Finance agent reading the ledger directly.Data: Management accounts stay inside the business. Private AI only.

Answer a tricky customer email properly

Customer service
Ask it like a colleague
A customer has sent this email [paste]. Check what they are actually asking, what they are feeling, and what we promised them before. Draft a reply that answers every point, owns anything we got wrong, and tells them exactly what happens next and when.
Brief it like an engineer
ROLE: senior customer service agent.
INPUTS: the full thread, the order or ticket record, our policy on refunds and replacements, previous promises made.
RULES: answer every question asked; no policy quoted at the customer, apply it; one apology where warranted, not three; give a date and a name for the next step; refunds are proposed, never promised, until approved.
OUTPUT: the reply, then a short internal note: root cause, whether a refund or gesture is recommended and why, and anything to fix so it does not recur.
QUALITY CHECK: every claim about the order matches the record; nothing promised that needs an approval we do not have.
On Adam AI: Customer service agent, with approval gates on refunds.Data: Customer emails contain personal data. Keep them off public tools.

Spot the patterns in last month’s complaints

Customer service
Ask it like a colleague
Here are last month's support tickets. What are people complaining about most, what is new compared with the month before, and which three fixes would remove the most tickets? Give me numbers, not impressions.
Brief it like an engineer
ROLE: service operations analyst.
INPUTS: ticket export (date, category, subject, first message, resolution, time to resolve), the previous month's export.
RULES: build categories from the data, not from the existing tags alone; show counts and share for each; separate volume from severity; quote two real examples per top category with identifiers removed.
OUTPUT: top ten categories with month-on-month change, the three root causes with estimated ticket reduction, and a one-paragraph summary for the owner.
QUALITY CHECK: category counts sum to the total; examples contain no personal data.
On Adam AI: Service analyst agent, from the help desk.Data: Export with names and emails removed before using any public tool.

Turn how we do a job into a written procedure

Operations
Ask it like a colleague
I'm going to describe how we [do the job] in my own words. Turn it into a clear step-by-step procedure a new starter could follow, with the checks, the exceptions and who to ask when it goes wrong. Ask me about anything I've skipped.
Brief it like an engineer
ROLE: process analyst.
INPUTS: a spoken or typed walkthrough from the person who does the job, screenshots if available, the systems involved.
RULES: one action per step; name the system and the screen; mark every decision point with its rule; list exceptions separately with who handles them; ask up to five clarifying questions before finalising.
OUTPUT: title, purpose, owner, trigger, steps table (step, system, action, check), exceptions, what "done" looks like, open questions.
QUALITY CHECK: no step assumes knowledge not given earlier in the procedure.
On Adam AI: Wingman, which learns the job by watching it done.Data: Safe on public AI if no customer or supplier details are included.

Reconcile supplier bills against what we agreed

Operations
Ask it like a colleague
Here are this month's supplier bills and here is what we have agreed with each supplier. Tell me which bills match, which are higher than agreed, and by how much, and draft the query email for anything that is wrong.
Brief it like an engineer
ROLE: purchase ledger controller.
INPUTS: supplier bills (supplier, period, lines, totals), agreements (supplier, product, agreed quantity, unit price, uplift terms, dates).
RULES: match on supplier and period, then line by line; tolerate rounding under 1 percent; apply agreed uplifts before flagging; never approve, only recommend.
OUTPUT: matched, over-billed and unexplained tables with the difference in pounds; one query email per supplier with the specific lines; a total of money at stake.
QUALITY CHECK: every flagged line shows the agreed figure beside the billed figure.
On Adam AI: Operations agent with the accounts and agreements connected.Data: Supplier pricing is commercially confidential. Private AI only.

Write a campaign from last month’s results, not a blank page

Marketing
Ask it like a colleague
Here is what we sent last month and how it performed. Tell me what actually worked, then write next month's campaign in the same voice, doubling down on what worked and dropping what didn't. Three emails and six social posts.
Brief it like an engineer
ROLE: B2B marketing lead.
INPUTS: last month's sends with open, click and reply rates, the posts with reach and engagement, our tone-of-voice notes, the offer for next month.
RULES: cite the result that justifies each choice; one call to action per piece; British English; no claims we cannot evidence; no superlatives about ourselves.
OUTPUT: a short "what worked" analysis with numbers, then the three emails (subject, preview, body) and six posts (platform, hook, body, CTA), and a two-line test plan.
QUALITY CHECK: every piece traces back to a result; nothing sounds like it was written by a machine.
On Adam AI: Marketing agent, from your own campaign data.Data: Campaign stats are fine on public AI; customer lists are not.

Check a web page before it goes live

Marketing
Ask it like a colleague
Here is the draft page [paste or link]. Read it as a busy business owner who has never heard of us. Tell me what is unclear, what is unproven, what is missing, and what they would want to do next. Then rewrite the opening so it earns the next ten seconds.
Brief it like an engineer
ROLE: conversion copy editor and plain-English reviewer.
INPUTS: page copy, the page's single goal, the audience, our proof points list.
RULES: flag any claim without proof; flag jargon with a plain alternative; keep the brand voice; no em dashes; headings must say something, not label something.
OUTPUT: findings table (issue, where, why it matters, fix), then the rewritten opening (headline, sub-heading, first paragraph, CTA), then a yes or no on publishing.
QUALITY CHECK: the rewritten opening makes no new claim that is not in the proof points.
On Adam AI: Marketing agent, with the proof points held in your knowledge base.Data: Draft copy is safe on public AI unless it names unannounced clients or products.

Decide whether a piece of work should be automated

Leadership
Ask it like a colleague
Here is a job we do every week [describe]. Should we automate it? Tell me what it costs us now in time and errors, what could go wrong if a machine did it, what must always need a person, and whether to start with a small trial. Be honest if the answer is no.
Brief it like an engineer
ROLE: automation analyst who is allowed to say no.
INPUTS: the process description, who does it, time per week, error rate if known, the systems it reads and writes, what happens when it goes wrong.
RULES: separate what the build may do alone (classify, draft, write back, send, escalate) from what must need a person; identify the integration prerequisites; recommend decline, counter or proceed with a reason; propose a shadow period where the AI only says what it would have done.
OUTPUT: verdict, the autonomy recommendation per step, the never-list, the integrations needed, the trial plan with a measure of success, and the honest risk in one sentence.
QUALITY CHECK: no recommendation to send to customers unattended on a first build.
On Adam AI: The My Builds analyst on Adam AI, which runs exactly this gate.Data: Safe on public AI as a description; the real data stays private.

Brief the board on our AI position

Leadership
Ask it like a colleague
Write me a one-page board note on where we are with AI: what we use today, where our data goes when we use it, the risks, what competitors are doing, and the two decisions the board needs to make this quarter. Plain English, no hype.
Brief it like an engineer
ROLE: chief of staff.
INPUTS: inventory of AI tools in use (official and unofficial), data classification policy, security incidents or near misses, competitor notes, budget envelope.
RULES: facts before opinions; every risk gets a current control and a gap; decisions framed as options with cost and consequence; no vendor names in the recommendation.
OUTPUT: one page: position, exposure, options, the two decisions, what happens if we do nothing.
QUALITY CHECK: a non-technical director can act on it without asking what a term means.
On Adam AI: Leadership agent, drawing on the company 2nd brain.Data: This note describes your security posture. Write it on private AI.
Is your AI a security risk?

Three rules before you paste anything into a public AI tool

Most of the prompts above are safe to try in a public chat tool. The data you put into them often is not. These three rules keep you out of trouble while you learn.

Names, money and health never go in

Customer names, email addresses, prices, invoices, payroll and anything about a person’s health or situation stay inside the business. Describe the pattern, not the record.

Assume it is kept

Treat anything typed into a public tool as sent to a third party and kept. If you would not email it to a stranger, do not paste it.

Check before you act

A fluent answer is not a correct one. Verify facts, figures and quotes against the source before anyone acts on them, every time.

Find out where your data goes today: the AI Readiness assessment

From prompt to skill

A prompt is a one-off. A skill runs every week.

On Adam AI the engineer register is the first draft of a skill: the role becomes an agent in your org chart, the inputs become connections to your systems, the rules become the never-list and the approvals, and the quality check becomes the release gate. Staff describe the job in their own words; the analyst turns it into the brief; the build runs in shadow beside the person until it is proven.

Over 1,000 skills ship with the platform, and every one of them started life as a brief like the ones on this page.

  • Describe the job once, in plain English
  • The analyst proposes the brief and the gates
  • Shadow mode: it says what it would have done
  • You mark it right or wrong until the evidence holds
  • Live on a small share first, widening as it proves itself

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