Prompting for business

Business prompts need business context

A business uses AI to save time on writing and thinking: product descriptions, social posts, support replies, competitor summaries, survey analysis. The model already knows a lot about marketing and business in general. What it doesn't know is your product, customers, policies, and brand. That's the context you have to supply — carefully, because some of it is confidential.

Marketing copy

Generic prompts produce generic copy: "Unlock the power of…", "Elevate your…". Good copy starts from specifics: who the customer is, what problem they have, and why your product solves it.

Write [type of copy] for [product/service].
Audience: [who they are + the problem they have].
Key benefit: [one main benefit].
Facts we can use: [features, prices, guarantees — only true ones].
Brand voice: [descriptors] — avoid [words/styles].
Format: [channel, length, number of variations].
Only use the facts provided. If something important is missing, list it as a question instead of inventing it.

Customer support drafts

AI can help support teams reply faster and more consistently — as long as replies are grounded in actual policy and checked by a person.

You're helping a support agent draft a reply. Use our policy below as the only source of truth.

Policy:
"""
[paste relevant policy, e.g. refund and shipping rules]
"""

Customer message:
"""
[paste message, with personal details removed]
"""

Draft a reply that:
- Acknowledges their frustration in one sentence (no over-apologizing).
- Explains clearly what we can and can't do, based only on the policy.
- Gives one concrete next step.
- Stays under 120 words.
If the policy doesn't cover their situation, say so in a note to the agent instead of making up an answer.

The key safeguard is the last line. Without it, a model may promise a refund or exception your policy doesn't allow — and a promise in writing is hard to walk back.

Business analysis

AI is a helpful thinking partner for analysis — SWOT, competitor comparisons, pricing options, or finding themes in customer feedback. Two rules keep it honest:

1. Give it your data. "Analyze customer feedback" means nothing without the feedback. Paste survey responses (anonymized) or summary numbers.
2. Separate facts from assumptions. Ask the model to label which points come from your data and which are its general reasoning.

Below are [number] anonymized customer survey comments about our product.
1. Group them into themes and count how many comments fall under each theme.
2. For each theme, include 1–2 representative quotes.
3. List the top 3 improvements suggested by the data.
Mark anything that's your own interpretation rather than directly supported by the comments.

Comments:
"""
[paste comments]
"""

As with any data work, spot-check the counts against the source — models can miscount, especially with long lists.

Confidentiality comes first

Some general guidelines:

• Prefer approved tools. Business and enterprise plans often have stronger data protections than free consumer accounts. Your IT or legal team can tell you which ones are approved.
• Never paste customer personal data, passwords, unreleased financial results, contracts under NDA, or trade secrets into a tool that isn't approved for them.
• Redact and generalize. Replace names with roles ("Customer A," "Supplier B") and remove account numbers. You can often round or approximate figures without losing the point.
• Check the output too. Make sure drafts don't accidentally include internal details meant only for you.

The pattern

Supply real facts, set the voice, restrict the model to what's true, keep a human reviewing anything external, and protect what's confidential. That's what makes AI useful in a business instead of risky.