What problem do content packs solve?
They close the gap between knowing a question matters and having something on the page that answers it. Most teams have a long list of questions buyers ask. Far fewer have the time to turn each one into a clear answer with the right markup.
A content pack does the first draft of that work. You pick a question, and you get a short answer, supporting content, an author bio and the structured data, all as HTML you can paste in.
Where do the questions come from?
Every pack starts from a real question, never a made-up one. QA Finder gathers them from several places.
- People Also Ask questions from Google results.
- Google autocomplete suggestions.
- Questions from Reddit threads, found through Google and through the threads AI engines cite.
- Questions pulled from enquiries or call notes you paste in, with personal details ignored and the text not stored.
That last source is often the most useful. The questions your sales team hears every week are usually the ones buyers also type into ChatGPT.
How does it work?
The steps are the same whether you want a pack or a full draft.
- Find the question. Run QA Finder for a topic, or start from a gap in content opportunities.
- Choose what to do with it. Add it to your prompt set to track, send it to Work as a task, or generate a pack.
- Generate the pack. You get ready-to-paste HTML with each of the five parts.
- Or generate a draft. For a task in Work, you can generate a full article draft instead.
- Edit and publish. A person reads it, adds real experience, checks the facts and puts it live.
- Track the result. Add the question to your prompt set so you can see whether AI answers change.
What is in a content pack?
Each pack has five parts. The table shows what each one is for.
| Part | What it is | Why it is there |
|---|---|---|
| Quick answer | A short, direct reply to the question | Gives engines a clear passage to quote |
| FAQ block | Related questions and answers, with FAQPage JSON-LD | Covers follow-up questions in one place |
| Q&A | A longer question and answer section | Adds depth and detail |
| Author bio | A short bio with Person schema | Shows who wrote it and why they know |
| Article JSON-LD | Markup for the page as a whole | Links the content, author and business |
The FAQPage and Article markup uses the same types as our schema generator, so it sits well with markup you already have.
What should you add before publishing?
A pack gives you the shape of a good answer. A few things make it yours.
- Real details from your business, such as prices, areas covered or lead times.
- An example from a job or a customer, written in your own words.
- The right author, with a bio that says why they know the subject.
- Links to the pages on your site that go further.
Those details are what separate a useful page from a generic one. They are also the parts a model cannot write for you.
What about full article drafts?
Some questions need their own page. For those, you can generate a full article draft from a task in Work. The task carries its evidence and links, such as the AI answers that cited other sites, so the draft starts with the right context.
Premium plans also include content rewrites. That suits cases where the answer belongs on a page you already have, rather than a new one.
Why does this matter for AI search?
AI engines tend to lift clear, direct answers to the question asked. A page that answers the question in its opening lines, names a real author and carries clean markup is easier to use. Our guide to FAQ content for AI search goes into what works and what does not.
We are careful here. A draft is a starting point, and it should never go live without a person reading it. The best pages add first-hand experience a model cannot know, such as prices you charge, jobs you have done and mistakes you see customers make. That is also what the experience and expertise signals in E-E-A-T are about.
Who uses it?
In-house content teams use packs to work through a backlog of questions quickly. Agencies use them to give clients a first draft to approve, which saves a round of briefing. Local firms often use the enquiry paste option, since their best questions come from phone calls rather than keyword tools.
Where it fits
Content packs sit between research and publishing. QA Finder finds the questions. Content opportunities shows where AI cites others and not you. Work holds the task, the evidence and the sign-off. AI citation tracking shows whether the new page starts to get cited.