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Knowledge-Base Article Draft Prompt Generator

⚡ Quick Answer

A knowledge-base draft prompt turns a resolved support ticket into a publish-ready help article: the problem in the customer's words, numbered steps, what success looks like, and what to try when it fails. The generator below follows that anatomy exactly and marks any step it cannot source from the ticket as [CHECK:].

⚡ Knowledge-Base Article Draft Prompt Generator

Configure your facts below to generate a production-ready, constraint-locked prompt.

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What this generator does

Every resolved support ticket is a knowledge-base article waiting to be written. The troubleshooting already happened: the customer's problem is described in their own words, the steps that worked are in the thread, the warnings and edge cases surfaced along the way. What usually stops that ticket becoming an article is the blank page — reformatting a messy thread into clean help-centre structure takes longer than solving the problem did. So the knowledge stays buried, and the next customer opens a new ticket instead of finding the answer.

This generator removes the blank page. Paste the resolved ticket — anonymised first, always — plus the confirmed fix in the agent's words, and the generated prompt produces a complete draft in proper help-article anatomy: a title in the customer's phrasing, a one-sentence summary that answers the question immediately, "Applies to" and "Last verified" header lines, prerequisites, numbered steps using the product's exact labels, a "What you should see" confirmation, an "If it didn't work" fallback, and the ticket's warnings preserved. And it carries the rule that makes the draft trustworthy: any step, label, or detail the ticket does not contain is written as [CHECK:] — a flag for you to verify against the product — never as a plausible-sounding invention your customers will follow into a dead end.

The anatomy of a help article that works

  1. Title in the customer's words. Customers search and browse with their phrasing ("My invoice won't download"), not your internal feature names ("PDF generation module"). The ticket hands you this phrasing for free — the generator keeps it and offers two alternatives.
  2. A one-sentence answer, first. The summary paragraph states the solution before any steps. Readers get the answer immediately; AI assistants quoting your help centre get a clean, self-contained passage to cite.
  3. "Applies to" and "Last verified" lines. Scope prevents the classic KB failure — a customer on the wrong plan or version following steps that cannot work for them — and the date tells readers how much to trust the article.
  4. "Before you start." The prerequisites the ticket revealed: account type, permissions, app version. One line here prevents the most common mid-article failure.
  5. Numbered steps, second person, present tense, exact labels. "Click Billing, then Invoices" — with the real names of buttons and menus, never paraphrases the model invented. Bold the UI elements; one action per step.
  6. "What you should see." A description of success after the final step, so the customer can confirm it worked instead of wondering.
  7. "If it didn't work" and "Good to know." The fallback checks from the ticket thread, plus its warnings and edge cases — the parts generic article generators drop, and the parts that stop the article itself generating tickets.

Who it's for

How it works

  1. Anonymise the ticket. Remove the customer's name, company, email, and any account data, screenshots descriptions, or identifying details. The article needs the problem and the solution — never the person.
  2. State the confirmed fix in your own words. One or two sentences from the agent who solved it. This is the article's ground truth; the prompt may structure it but never extend it.
  3. Set the scope. Which plans, versions, or platforms the solution covers. Unknown scope produces a [CHECK:] in the header rather than a silent assumption.
  4. Copy the generated prompt and run it in Claude Sonnet 5.5, Gemini 4 Argon, or GPT-6.1 / GPT-4o.
  5. Resolve every [CHECK:] against the product before publishing. Click through the steps yourself, confirm each label, fill each gap — then set the "Last verified" date to the day you did it.

The prompt template

PROMPT TEMPLATE 
You are a technical writer for a customer help centre. Draft a knowledge-base article from the resolved support ticket below.

CONTEXT — use only what is supplied here:
- The resolved ticket, anonymised (the customer's name, company, email, and account data were removed BEFORE pasting): [paste]
- The confirmed fix or answer, in the agent's words: [paste]
- Product area: [e.g. Billing / Mobile app / API]
- Applies to: [plans / versions / platforms this solution covers / not stated]

INSTRUCTIONS:
1. Title the article the way a customer would phrase the problem — their words, not internal feature names. Give the main title plus two alternatives.
2. Open with a one-sentence summary that answers the problem directly. A reader — or an AI assistant quoting this article — must get the answer from the first paragraph.
3. Add the header lines "Applies to: [scope]" and "Last verified: [today's date]".
4. Add a "Before you start" line listing what the reader needs (account type, permissions, app version) — only if the ticket supports it.
5. Write the solution as numbered steps: second person, present tense, one action per step, using the exact names of buttons, menus, and settings as they appear in the product. Follow the steps with "What you should see", describing the successful result.
6. Add an "If it didn't work" section with the next check or fallback drawn only from the ticket, ending with how to contact support.
7. Carry every warning, limitation, and edge case from the ticket into a "Good to know" section.
8. Any step, label, or detail the ticket does not contain must be written as [CHECK: detail needed] — never guess a step, a menu name, or a setting.

NEGATIVE CONSTRAINTS:
- Never invent product steps, settings, prices, policies, or timelines.
- Never include the original customer's name, company, data, or story — the ticket is a source, not an example.
- Do not extend the solution beyond the "Applies to" scope, and do not imply it works for versions or plans the ticket does not cover.

OUTPUT FORMAT: Title (3 options) → One-sentence summary → Applies to / Last verified → Before you start → Steps (numbered) → What you should see → If it didn't work → Good to know → Related articles (suggest 2 topics).
Best model for this: Claude Sonnet 5.5 for clear, customer-worded instructional writing. Gemini 4 Argon when the source is a long, multi-message ticket thread; GPT-6.1 if you need many drafts in a consistent house format.

Worked example

The source (resolved ticket, anonymised before pasting): A Pro customer reported that clicking "Download PDF" on an invoice made the tab spin forever with no file. The agent found the invoice opens as a PDF in a new browser tab and the customer's pop-up blocker was killing it; allowing pop-ups for the app domain fixed it immediately. The agent also noted the invoice list lives under Billing → Invoices, and emailed invoices come from the billing sender address.

Output excerpt:

Title options: "My invoice won't download" · "Invoice PDF opens a blank tab" · "How to download an invoice as a PDF"
Summary: Your invoice downloads as a PDF in a new browser tab — if the tab spins or opens blank, your browser is blocking the pop-up that delivers it.
Applies to: All plans · Web app · Last verified: [date of your check]
Before you start: You need billing permissions on your account to see invoices.
Steps:
1. Go to Billing, then Invoices.
2. Find the month you need and click Download PDF.
3. If a new tab opens and stays blank, click the pop-up blocked icon in your browser's address bar and allow pop-ups for this site.
4. Click Download PDF again.
What you should see: The invoice PDF opens in the new tab, ready to save.
If it didn't work: Try the same steps in a private/incognito window. Still stuck? Contact support and mention that the pop-up fix did not work.
Good to know: Emailed invoices are sent from [CHECK: confirm the billing sender address before publishing].

One [CHECK:] survived to the excerpt — the sender address came from an agent's memory, not the ticket, so the draft flags it for confirmation rather than printing it as fact. Multiply that discipline across a whole help centre and you get articles customers can actually follow.

Guardrails

Frequently Asked Questions

Practical answers covering model choice, workflow integration, and compliance rules.

Start with the two assets the ticket already contains: the customer's own description of the problem (which becomes the title and framing) and the confirmed fix (which becomes the steps). Anonymise the ticket, state the fix in the agent's words, and run a structured prompt like the one on this page: it reformats the material into help-article anatomy — answer first, prerequisites, numbered steps, success check, fallback, warnings. Your job after the draft is verification, not writing: click through the steps, resolve the [CHECK:] flags, set the verified date, and publish.

The anatomy used here: a title in the customer's words; a one-sentence summary that answers the question outright; "Applies to" scope and "Last verified" date lines; a "Before you start" prerequisites line; numbered steps in the second person with exact UI labels; a "What you should see" success confirmation; an "If it didn't work" fallback; and a "Good to know" section for warnings and edge cases. The order is deliberate — answer before procedure, scope before steps — because readers arrive mid-problem and scan; structure is what lets them find the one paragraph they need.

Two mechanisms, both in this template. First, ground it: the model may only use the ticket and the agent's stated fix, and the instructions say so explicitly. Second, give invention somewhere honest to go: the [CHECK:] marker. A model told "never guess" with no alternative will still guess under pressure to produce a complete-looking article; a model told "write [CHECK: detail needed] wherever the source runs out" flags its gaps instead. Then make resolving every flag a non-negotiable step in your publishing checklist — the marker only works if a human always closes it.

As long as the problem requires, and no longer — but the two common shapes differ. A simple FAQ-style article answering one narrow question should be short: a direct answer and a few lines of context, roughly the length of a couple of paragraphs. A troubleshooting article earns more length because it carries prerequisites, steps, a success check, a fallback, and warnings. The failure mode to avoid is padding: if a paragraph neither answers, instructs, nor warns, delete it.

Yes, and the discipline overlaps almost completely with writing for humans. Answer-first structure, one question per article, explicit scope ("Applies to"), a visible last-verified date, and exact terminology all make an article easier for an AI assistant to retrieve and quote accurately — and easier for a scanning customer to use. The generator builds these in by default, so an article drafted here serves your help centre, your support chatbot, and AI search results from the same text.

Not until the data is out. Anonymise first: strip names, company names, emails, account IDs, and any detail that identifies the customer, then draft from the cleaned text. This is non-negotiable for two reasons — the draft may pass through a third-party AI tool when you run the prompt, and drafts have a habit of being pasted into articles verbatim, publishing someone's data to your public help centre.

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