Customer Question to Honest LinkedIn Post
Turn a real customer question and verified context into a clear, anti-hype LinkedIn answer post.
Customise & copy
Add the context you have. The prompt updates as you work.
Paste the customer's question exactly as received.
Provide only verified facts, limitations, conditions, and relevant technical context.
Describe who should understand the answer and their relevant industry or situation.
Explain the product or service context needed to make the answer accurate.
Choose the maximum length for the finished LinkedIn post.
Live prompt
5 fieldsWrite one plain, honest LinkedIn answer post based only on the information provided. Customer question: {{customer_question}} Fact-based answer and technical context: {{answer_details}} Target reader or industry context: {{target_reader}} Product or service context: {{product_context}} Desired length: 150–250 words Start directly with the question or relevant context. Answer clearly, explain what the answer means for the target reader, and keep the wording useful and specific. Do not invent facts, testimonials, social proof, examples, results, or technical details. Do not use hype openers, engagement bait, unnecessary politeness, step-by-step theatre, hashtags, or a call to action asking people to comment, share, or message. Return only the finished LinkedIn post as plain text.
Proof before you use it
A real example
Tested on gpt-5.6-luna on 2026-09-09
{
"target_reader": "Owners and general managers of independent restaurants trying to control food costs.",
"answer_details": "Software can improve ordering decisions by showing usage patterns, spoilage, and stock levels, but it does not eliminate waste on its own. Results also depend on accurate data entry, staff routines, supplier schedules, portion controls, and menu planning. The software is most useful when it supports an existing process for reviewing and acting on the data.",
"product_context": "We offer inventory software for restaurants with purchasing, stock tracking, and waste-reporting features. We do not provide kitchen operations consulting or guarantee a specific reduction in waste.",
"customer_question": "Can a restaurant reduce food waste just by installing inventory software?",
"length_constraint": "Up to 150 words"
}A small ritual that works
How to use it
- 01Paste the exact customer question.
- 02Add verified answer details and technical context.
- 03Describe the target reader and product or service context.
- 04Choose the desired length and review the finished post.
Why it works
The prompt gives the writer a clear source of truth: a customer question, verified answer details, audience context, product context, and a length limit. It also defines the intended format and tone in practical terms. The restrictions against invented facts, hype, social proof, engagement bait, and unnecessary calls to action keep the post focused on answering the question rather than turning it into promotional copy. Requiring the response to begin with the question or relevant context creates a direct opening, while asking what the answer means for the target reader connects technical information to the intended audience.
Where it fails
The prompt cannot compensate for incomplete, vague, outdated, or contradictory information in the supplied fields. It also limits the writer to the provided facts, so it is not suited to questions that require independent research, detailed argumentation beyond the supplied context, or claims about results that have not been documented. The product context may also be too general to support a precise explanation of responsibilities, limitations, or technical boundaries.
Customisation tips
Make the answer_details field the most specific field in the setup. Include verified conditions, exclusions, responsibilities, relevant limitations, and technical distinctions rather than broad talking points. Define the target_reader by role, level of expertise, industry, and decision context. Use product_context to clarify what the organization provides, what it does not provide, and which responsibilities remain with the customer. Choose the length_constraint after deciding how much explanation the question genuinely needs. If the post should use a particular spelling convention or terminology, add that preference to the relevant context fields without weakening the instruction to remain factual.
Watch-outs
- The prompt asks for a LinkedIn post, but its restrictions rule out many common platform conventions, including hashtags, engagement prompts, and promotional calls to action.
- The answer_details field should distinguish verified facts from assumptions, interpretations, or desired messaging.
- Product context can introduce commercial bias if it is written as sales copy rather than as a factual description of scope, responsibilities, and limitations.
- A short length constraint may leave insufficient room for important qualifications, so the supplied facts should be prioritized before selecting the target length.
Works in
Was this useful?
Submit your variation
If your version helps, it may be published with your name and a link back to you.
Want to make AI useful across your team? Explore Atul's training.