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ChatGPT can quickly prepare a draft, organize information, compare options, or identify patterns in the data you provide. The quality of the result, however, depends on more than the model. A one-line request without context usually produces a generic response that may contain unsupported assumptions and may not reflect how the company actually works.
For business, the useful asset is not a “magic prompt” but a repeatable process: provide verified context, define the output, limit assumptions, review the draft, check the facts, and keep a person responsible for approval. The broader guide to AI for business maps the main use cases, risks, and rollout steps.
The 25 templates below can be adapted to real work. Replace every item in square brackets with your own information. Do not submit confidential material unless the service, plan, workspace settings, contracts, and company policy explicitly allow it.
A prompt combines an instruction, context, and source material for the model. It does not need to be long, but it should remove the most important ambiguity.
Instead of “write website copy,” clarify:
which page you need;
who will read it;
what problem the offer solves;
which facts are approved;
which claims must not be added;
which structure the page should follow;
what the reader should do next;
how missing information should be marked.
ChatGPT produces plausible language, but plausibility is not evidence. Prices, product specifications, laws, statistics, quotations, links, and service terms require separate verification.
A practical prompt can contain seven blocks:
Role — the type of professional help required.
Task — one specific action and output.
Context — product, audience, market, and decision stage.
Inputs — copy, tables, feedback, policies, and verified facts.
Constraints — what must not be invented or changed.
Format — table, list, structure, length, language, and tone.
Review — assumptions, weaknesses, and open questions to flag.
Universal template:
Help as [role or type of expertise].
Task: [one specific deliverable].
Context: [company, product, audience, market, and decision stage].
Inputs: [verified facts or source material].
Constraints: do not invent facts, prices, statistics, cases, or guarantees. If information is missing, ask a question or mark the gap.
Output format: [structure, length, language, and tone].
Before the final answer, check the logic, contradictions, and unsupported claims.
Do not copy a template mechanically. First prepare a source of truth: an approved service description, benefits, terms, analytics, customer feedback, or company rules. If the business has not yet defined how it creates demand, begin with the guide to getting customers for a small business.
For every template:
replace all variables in square brackets;
remove data that the selected service is not allowed to process;
add an example of the desired output when available;
ask the model to begin with clarifying questions;
review the answer against your own acceptance criteria;
save a successful version as a governed team template.
Use anonymized enquiries from forms, conversations, call notes, or reviews. The model should find patterns in the supplied evidence rather than inventing customer pain points.
Analyze the anonymized customer enquiries below.
Group them by topic, intent, and decision stage.
For each group, provide the number of mentions, a typical phrase, the main question, and a possible page or FAQ that should answer it.
Do not add problems that are absent from the source data.
List messages that cannot be classified confidently in a separate section.
Data: [paste anonymized enquiries].
Use the supplied interviews, enquiries, and reviews to create working audience segments.
For each segment, describe the situation, job to be done, selection criteria, objections, proof required, and desired next step.
Quote only the supplied language. Do not invent demographics or motivations.
Finish with a list of missing evidence needed for a reliable conclusion.
Materials: [data].
Create a [number]-week content plan for [company/product].
Audience: [segments]. Goals: [SEO, trust, enquiries, retention].
Approved topics and search queries: [list based on verified research].
Organize content by stage: problem, comparison, selection, decision, and use.
For every item, provide a working title, primary query, intent, format, main point, proof required, and CTA.
Do not estimate search volume without supplied data.
The output is a working draft, not a substitute for aligning the offer, SEO, UX, and design. That alignment forms part of professional website development.
Prepare the structure and first draft of a service page for [service].
Audience: [who]. Problem: [what needs to be solved].
Verified service facts: [facts].
Structure: hero, problem, solution, process, outcome, evidence, FAQ, and CTA.
Write specifically. Avoid clichés such as “innovative” and “best,” and do not add unsupported guarantees.
Insert [DATA REQUIRED] where price, timing, evidence, or a service term is missing.
Turn the supplied article into five separate assets: a LinkedIn post, Facebook post, short-video script, email, and sales talking points.
Preserve the facts and core position, but adapt the opening, length, rhythm, and CTA to each channel.
Do not add new figures or promises.
After each version, identify the source section it relies on.
Article: [text or verified source].
ChatGPT can structure collected research, but it does not replace search data or a technical audit. Professional SEO services cover page mapping, crawlability, indexing, content, authority, and measurement.
Create an SEO content brief for [topic].
Primary query: [query]. Supporting queries: [list].
Search intent: [informational/commercial/mixed].
Competitor pages and my notes: [data].
Propose an H1, H2–H3 outline, FAQ questions, evidence requirements, internal links, and cannibalization risks.
Do not invent volume or ranking data. Separate essential coverage from optional sections.
Draft search-ad variants for [service].
Query intent: [intent]. Segment: [audience]. Geography: [market].
Landing-page facts: [approved points].
Character limits: [limits]. Prohibited claims: [list].
Create [number] headlines and [number] descriptions grouped by angle: outcome, process, trust, and urgency.
Do not use unverified discounts, guarantees, awards, or status claims.
The final set still needs platform-policy review, and campaign data must select the winner. This can be handled as part of professional Google Ads management.
Review the Google Ads search-terms report.
Classify every term as relevant, uncertain, or irrelevant.
For every irrelevant term, propose a negative keyword and whether it belongs at campaign or ad-group level.
Do not reject a term only because it has no conversions without considering impressions and clicks.
Show ambiguous cases separately for specialist review.
Services and exclusions: [description].
Report: [table].
Review the copy for this [page type] and audience [who].
Assess intent match, offer clarity, specificity, logic, evidence, objections, CTA, repetition, and unsupported claims.
Return a table with the problem, excerpt, why it matters, and proposed revision.
Do not rewrite everything yet. Prioritize the five most important changes first.
Copy: [paste copy].
When a page attracts traffic but few enquiries, verify acquisition sources, analytics, and the user journey before rewriting random elements. The guide to why website traffic does not generate leads explains that diagnosis in more detail.
Develop CRO hypotheses from the supplied page evidence.
Inputs: traffic sources [data], conversion [data], session-review findings [data], survey responses [data], and page copy [text].
For each hypothesis, provide the observation, possible cause, proposed change, expected signal, primary metric, risk, and priority.
Do not present an assumption as a fact or forecast an exact uplift without an experiment.
Analyze the anonymized enquiry and prepare a card for a salesperson.
Extract only explicit information: need, product, budget, timing, geography, constraints, and next step.
Mark every missing field as “unknown.”
Propose up to five clarifying questions in a natural order.
Do not infer a person's ability to pay or reject the enquiry automatically.
Enquiry: [text].
Create a one-page briefing for a meeting with [company/client].
Use only supplied material and verified public sources.
Structure: context, previous communication, likely objectives, open questions, agenda, desired outcome, and risks.
Separate facts from assumptions. Turn every assumption into a question to verify.
Materials: [data].
Prepare a proposal draft from the brief.
Structure: client situation, objective, proposed solution, scope, stages, outcome, timing, price, assumptions, exclusions, and next step.
Use only approved services, prices, and terms.
Do not add guarantees, discounts, or features that are absent from the inputs.
Flag every contradiction and field that requires salesperson approval.
Brief: [data]. Terms: [data].
Write a concise follow-up after the meeting.
Tone: professional and human, without pressure.
Structure: thank you, confirmed objective, agreed decisions, owners, dates, materials, and next action.
Do not add agreements that are absent from the notes.
Flag ambiguous wording for review.
Notes: [text].
Group customer objections found in the anonymized conversations.
For each one, prepare the possible concern behind it, a clarifying question, a concise response, evidence required, and a situation where the team should not push further.
Do not use manipulation, fake scarcity, or pressure.
Follow the brand communication style: [description].
Conversations: [data].
Create an FAQ from approved pages, instructions, and customer questions.
Merge duplicates without removing important conditions.
For every answer, identify its source as [document/section].
If the materials do not support an answer, add the question to an “expert answer required” list instead of guessing.
Materials: [data].
Draft a response to the customer enquiry below.
First acknowledge the issue briefly, then provide only verified steps from the knowledge base.
If the case falls outside the rules, do not invent a solution; prepare a handoff to a person.
Do not promise a refund, compensation, or resolution date unless the policy confirms it.
Enquiry: [anonymized text].
Knowledge base: [extract].
Brand tone: [description].
The model can normalize attributes and prepare drafts, but the approved catalog must remain the source of truth. Categories, filters, and record rules should be designed during online-store development.
Create a product record using only the supplied specifications.
Format: short name, 3–5 benefits, description, specification table, package contents, care, SEO Title, Meta Description, and Alt text.
Do not invent materials, compatibility, certificates, warranty, or country of origin.
Write “confirmation required” for every missing attribute.
Keep attribute names consistent across all products.
Product data: [structured data].
Analyze the anonymized reviews for [period].
Group positive signals, recurring problems, expectations, and suggestions.
For each theme, provide a mention count and short excerpts without personal data.
Separate systemic issues from isolated cases.
Suggest actions, but label them as hypotheses rather than proven solutions.
Reviews: [data].
Analyze this complaint for internal escalation.
Structure: confirmed facts, customer claims, missing information, potential risk, responsible team, and recommended next step.
Do not assign blame or make a legal conclusion.
Prepare a neutral customer message confirming that the case has been sent for review, without unsupported promises.
Complaint and history: [anonymized data].
Turn the meeting notes into a structured summary.
Extract decisions, open questions, actions, owners, dates, dependencies, and risks.
Do not assign an owner or deadline unless it was stated; write “to be agreed.”
Finish with a short confirmation message for all participants.
Notes: [text].
Turn the expert notes into a standard operating procedure.
Structure: purpose, scope, prerequisites, roles, step-by-step actions, quality checks, exceptions, escalation, and review date.
Do not fill gaps with assumptions. Collect them under “questions for the process owner.”
Notes: [data].
Analyze the table in relation to this business question: [question].
First check field names, units, date ranges, missing values, duplicates, and obvious anomalies.
Then show calculations, filters, and findings.
Separate facts from possible explanations. Do not infer causation from correlation alone.
Finish with three checks that could disprove the conclusion.
Table: [file or data].
The source systems must be configured before a reliable report can be produced. Website events and conversions, for example, should be defined in Google Analytics 4.
Prepare a weekly leadership report from the supplied evidence.
Structure: what changed, primary metrics against plan and the previous period, causes only when supported, risks, decisions, owners, and next steps.
Do not hide missing data behind general language.
Identify the source and period for every number.
Plan: [data]. Actuals: [data]. Team comments: [data].
Prepare a concise decision memo for [question].
Describe the objective, criteria, available options, costs, benefits, risks, dependencies, and reversibility.
Use only supplied evidence. Do not create false precision where uncertainty exists.
Provide a recommendation with reasoning and the conditions under which another option should be selected.
List facts that must be verified before approval.
Materials: [data].
Create a short acceptance checklist for every repeated task. It may verify that:
every figure matches the source;
product names, prices, and dates are current;
no links, quotations, or case studies were fabricated;
facts and assumptions are clearly separated;
the requested structure and length were followed;
the copy matches the brand voice;
prohibited claims are absent;
personal data has been removed or is processed lawfully;
the responsible specialist reviewed the result;
the final approver is known.
Test a prompt against several cases: simple, typical, incomplete, and difficult. One successful output does not prove that a workflow is reliable.
The exact rules depend on the service, plan, workspace controls, contracts, and company policy. Define allowed, restricted, and prohibited information before employees begin using the tool for work.
Without a proper environment and explicit permission, do not submit:
passwords, API keys, or access codes;
payment or medical information;
identifiable customer or employee data;
private correspondence without a lawful basis;
trade secrets;
proprietary code or client documents;
confidential legal material;
information whose disclosure is prohibited by contract.
Anonymization is more than replacing a name. A unique position, address, order number, date, or combination of details may still identify a person.
Do not store dozens of random prompts without ownership. Record the following for every approved template:
| Field | What to document |
|---|---|
| Name | a specific task, not “marketing prompt” |
| Owner | who keeps it current |
| Allowed data | what may be submitted |
| Prohibited data | what must not be submitted |
| Required inputs | documents and fields |
| Output format | structure and example |
| Review | acceptance criteria |
| Approval | who makes the final decision |
| Version | date and change history |
Begin with three tasks that consume time regularly and have a manageable cost of error. Compare time, revision count, and quality before and after using the template. Once a workflow is stable, it may be connected to the website, CRM, or internal knowledge. To discuss such an implementation, contact BB STUDIO.
A generic instruction ignores the sources, risks, and acceptance criteria of a specific process. Templates require adaptation.
The model can produce a convincing figure or example. That does not make it real.
Treat it as a draft. Review, shorten, clarify, and verify it before use.
Without a structure, the team receives inconsistent results that are difficult to compare and repeat.
More data does not automatically create a better answer. Supply only what is necessary for the task and allowed by policy.
The greater the cost of an error, the stronger the human-control requirement. Publication, customer communication, financial actions, and legal decisions require separate approval.
ChatGPT creates business value through a disciplined process, not a single perfect phrase. A useful prompt defines context, sources, constraints, format, and review. A person remains responsible for facts, decisions, and consequences.
Choose one template, add your own approved data, test it on several cases, and measure the result. Then document the successful version, owner, and usage rules. This is how isolated experiments become a governed team capability.
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