Key takeaways
- A standard SEO brief (keyword, intent, SERP notes, word count) targets a ranked slot. An AI search brief targets a set of prompts, the sub-queries engines fan out, and the sources that already get cited.
- Fan-out depth moves around a lot, so re-pull fan-outs for each prompt before you write each brief. Do not hard-code a number of sub-queries.
- Put the prompt's intent (commercial, informational, branded) in the brief and set the required format from it. ChatGPT cites different content types for each.
- Run a citation gap check on every brief: which URLs get cited for this prompt, which are yours, and which competitor pages win instead.
- The template below has nine fields. You can fill it by hand, with a spreadsheet, or with a platform that already holds your prompt and citation data.
Why the old brief stops working
Open any guide to writing a content brief and you get the same skeleton: primary keyword, search intent, a few notes on what ranks, internal links, word count. It is a decent template. It has aligned writers and strategists for twenty years.
It is also built to win a ranked position on Google. A growing share of buyers now ask ChatGPT or Google's AI Overviews and get one synthesized answer that names a handful of sources. There is no position 4 to climb to. You are either in the source list or you are not.
Profound's piece on AI content briefs (24 Aug 2026) makes the same argument. Its line that stuck with me: AI search runs on prompts, and prompts aren't compressed the way keywords are. A buyer types a full sentence with context, and the engine then breaks that sentence into several searches of its own. A brief that starts at a two-word keyword has already thrown away most of the signal.
To be fair, Profound is a vendor, and its claim of a 1.9B+ prompt dataset is its own number. The reasoning holds up without it, though.
What a brief is actually for
A brief settles the decisions a writer would otherwise guess at. Who is this for? What angle separates it from what already exists? What claim should it make? It is different from an outline: an outline is structure, a brief is the reasoning that makes that structure correct.
None of that changes for AI search. What changes are the inputs. Three data sources replace the keyword and the SERP screenshot:
- Prompt data: the questions buyers actually run, with volume and difficulty where you can get them.
- Fan-out data: the sub-queries the engine fires off to answer each prompt.
- Citation data: which URLs get retrieved and cited today, and where you are missing.
Input 1: prompt data
Start from prompts, not keywords. A keyword like "crm small agency" becomes a prompt like "What's the best CRM for a 10-person marketing agency that does a lot of retainers?" The second one carries a persona, a size, a use case and a buying intent. That is what your page has to answer.
Good sources for target prompts:
- Your own Search Console queries, rewritten as full questions
- People Also Ask and tools like AlsoAsked or AnswerThePublic
- Sales call transcripts and support tickets (usually the best source, and free)
- A prompt tracking tool that suggests prompts and shows volume and difficulty
For each prompt, tag the intent. This sounds minor and it drives the format decision later. Promptwatch Data (Sep 28 to Oct 4, 2026) shows ChatGPT's citation mix shifts a lot by intent, which we cover below.
Input 2: fan-outs
When you ask ChatGPT something that needs the web, it does not search your exact sentence. It splits it into one or more shorter searches. Google calls the equivalent behavior query fan-out in AI Mode. Each sub-query is a separate retrieval your content can win or lose.
Here is the part that matters for briefs: fan-out behavior is unstable. Promptwatch's ChatGPT query fan-out data shows average fan-outs per response falling from 2.15 in early December 2025 to about 1.84 by early March 2026, then coming back at exactly 1.0 in April. The average query length shrank too, from about 117 characters to roughly 53 characters (6 to 8 words) in April.
Then on August 8, 2026, ChatGPT started using the site: operator at scale. The share of fan-out queries using it jumped from 0.37% to 16.8% in one day, and average fan-outs per response rose from about 1.08 to about 1.83. Those site: searches are added on top of the generic ones.
Two lessons for your template:
- Never write "cover these 6 sub-queries" as a fixed rule. Write "re-pull fan-outs for this prompt on the day you brief it." Mappings built before August 8 are stale.
- Your own domain is now a retrieval surface. A site: query can only return what search engines have indexed, so crawlability and indexing belong on the brief checklist, not just in a technical audit.
The typical fan-out splits into recognizable shapes: "X vs Y" comparisons, pricing, alternatives, how-tos, and constraint-specific variants ("for small agencies", "in 2026"). Promptwatch's advice is to build a cluster of focused pages rather than one mega-page, and to write headings that read like search queries ("Best CRM for small agencies 2026") with the entity and category first.
If you want to see fan-outs directly, the free GEO Inspector Chrome extension from Promptwatch shows them inside ChatGPT, including fetched versus cited sources. There is also a community walkthrough on YouTube, "Query Fanouts: How to Track, Analyze, and Create Content using Fanouts for AI Search Visibility" by Jay's AI SEO Tips, if you prefer video.
Input 3: citation gaps
This is the input most briefs skip. Before you write anything, look at who already gets cited for the prompt.
Peec AI's source gap guide (Jan 30, 2026) defines a citation as a URL that was retrieved and used in the answer, and the method is simple. Set up a tagged prompt project, filter citation sources for your domain, then compare the most cited and least cited URLs. Its approach works in any tracker.
For a single brief, run a mini version:
- List every URL cited for the target prompt and its fan-outs.
- Mark which are yours, which are competitors, and which are third parties (review sites, Reddit, YouTube, marketplaces).
- Classify each by content type.
- Ask why the winners win: a table, fresher data, a clearer definition, a stronger entity match in the title?
- Decide whether you can beat a competitor page or whether you need a presence on the third-party source instead.
Step 5 changes the brief. Sometimes the right deliverable is not a blog post. It is a listing update, a review profile, or a Reddit answer. Do not bet on Reddit too heavily without checking; Promptwatch Data shows reddit.com's share of ChatGPT citations collapsing from about 4% to 0.5% on August 14, 2026, while AI Overviews decline more gradually. And on the same data, ChatGPT drew just 0.46% of citations from social platforms versus 12.2% for Google AI Overviews (Sep 28 to Oct 4, 2026). Which engine you target changes which third-party sources matter.
Set the format from the intent
Here is where the intent tag pays off. Promptwatch's ChatGPT citation share by content type (Sep 28 to Oct 4, 2026) breaks down like this:
| Content type | All prompts | Commercial | Informational | Branded |
|---|---|---|---|---|
| Product pages | 22.4% | 26.0% | 19.1% | 23.3% |
| Landing pages | 16.8% | 20.6% | 13.9% | 22.8% |
| Listicles | 13.6% | 15.0% | 13.6% | n/a |
| How-tos | 8.7% | n/a | 10.7% | n/a |
| Documentation | 7.5% | n/a | 8.5% | n/a |
| Comparisons | 5.9% | 7.9% | n/a | n/a |
| Reviews | 1.7% | n/a | n/a | 5.6% |
Cells marked n/a were not broken out in the figures I have. A few things jump out. Four formats (product pages, landing pages, listicles, comparisons) make up almost 70% of commercial citations. Informational prompts lean harder on how-tos and docs. Branded prompts cite reviews at more than three times their overall share. So a brief for "best X for Y" should demand a comparison or listicle plus a strong product or landing page. A brief for a branded query should ask for review coverage.
Freshness matters too. Promptwatch's content half-life report says news loses half its cited pages within about 36 days, while landing pages and docs stay cited for months. Put a refresh date in the brief for anything news-like, and treat evergreen pages as assets you maintain.
The template
Copy this into Notion, Google Docs or your CMS. Nine fields, each tied to a data source.
AI SEARCH CONTENT BRIEF
1. Target prompt(s)
Primary prompt (full sentence):
2 to 4 variant prompts:
Persona / buyer stage:
Intent: commercial | informational | branded
Monthly prompt volume + difficulty (if available):
2. Fan-out map (re-pulled on: __/__/____)
Engine(s) checked: ChatGPT | AI Mode | Perplexity | other
Sub-queries observed:
- comparison (X vs Y):
- pricing / cost:
- alternatives:
- how-to / setup:
- constraint variants (size, region, year):
- site: queries observed? yes/no
Which sub-queries does this page own?
Which need a separate supporting page?
3. Citation landscape
URLs cited for the prompt + fan-outs:
Ours: ___ Competitors: ___ Third-party: ___
Content type of each winner:
Why they win (data, freshness, structure, entity match):
Gap we can fill:
4. Required format
Primary format (from intent mix):
Supporting formats (table, FAQ, comparison block):
Heading style: entity + category first, query-like
5. Core claim and angle
The one claim this page must make:
Proprietary data, quotes or examples we can add:
6. Answer block
Direct answer in 40-60 words, placed under the first H2:
Facts that must appear verbatim (pricing, specs, dates):
7. Sources and entities
Sources to cite (prefer first-party and primary):
Entities to name consistently:
Third-party presences to update alongside (reviews, listings, docs):
8. Technical checks
Page crawlable and indexed (site: check):
AI crawlers not blocked in robots.txt:
Schema type:
Internal links in / out:
9. Measurement and refresh
Prompts to track after publishing:
Success metric: cited within __ weeks | position in source list | AI-referred visits
Refresh date:
How to fill it in fast
Fields 1 to 3 are the expensive ones, because they need live data. Fields 4 to 9 are decisions you make once you have it. If your research step takes longer than your writing step, that is normal for the first month and a sign the process is working.
Tools that can fill the template
You have three options: a spreadsheet and manual checks, a classic content-brief tool, or an AI visibility platform that already holds prompt, fan-out and citation data. A few notes on where each fits.
Promptwatch is the one I would start with for fields 1 to 3, because prompt volumes and difficulty, query fan-outs and citation data sit in one place, and you can turn a citation's prompts straight into a content brief. Its Content Agent can then write and publish against the gaps, and track whether the page gets crawled and cited. Plans start at $95/month (check promptwatch.com/pricing).
Profound sells a brief-generation agent built on its prompt and citation dataset, aimed at larger teams.
Peec AI is good for the source gap analysis step, though it is monitoring-only. You would still write the brief and the content yourself.
LLM Pulse positions its GEO content writing around briefs built from what the models research.
Classic brief tools still help for the SEO half of the work. Content Harmony, Dashword and Frase generate structured briefs from SERP data, and MarketMuse is strong at topic coverage. None of them show you AI citations, so treat them as the lower half of the brief, not the whole thing.
| Approach | Prompt data | Fan-outs | Citation gaps | Best for |
|---|---|---|---|---|
| Spreadsheet plus manual ChatGPT checks | Manual | Manual (browser extension) | Manual | Solo operators, a few briefs a month |
| Classic brief tools (Content Harmony, Frase, Dashword) | No | No | No | SERP-driven structure and outlines |
| Monitoring-only trackers (Peec AI, Otterly.AI) | Yes | Varies | Yes | Spotting gaps, then briefing by hand |
| Visibility platforms with content workflows (Promptwatch, Profound) | Yes | Yes | Yes | Teams producing briefs and content at volume |
If you want to compare more options, the GEO software directory at bestgeosoftware.com lists the category.
Can AI write the brief for you?
Partly. Given prompt data, citation data and your site, an agent can assemble a writer-ready brief faster than a person juggling five tabs. But a general chatbot with no live data will happily invent fan-outs and citations. If you use ChatGPT as the drafting engine, feed it the pulled data from fields 1 to 3 and ask it to fill fields 4 to 9. That is a good use. Asking it to guess what gets cited is not.
One Reddit prompt that circulates in r/SEO asks ChatGPT to hunt for logical gaps and hidden assumptions in a site's content. It is a decent sanity check on a finished brief, just not a source of ground truth.
Common mistakes
- Briefing from one prompt. Buyers phrase things many ways. Use a primary prompt plus a few variants.
- Hard-coding fan-out counts. See the 2.15, 1.84, 1.0, 1.08, 1.83 swings above.
- Using one format for every intent. Reviews matter for branded prompts and barely register elsewhere.
- Ignoring crawlability. If AI crawlers get errors on your page, a perfect brief is wasted. IP-verified crawler logs and a weekly site audit help here.
- Never going back. Add the refresh date to the brief, and re-run the citation check 4 to 6 weeks after publishing.
A short workflow to adopt this week
- Pick five prompts you care about and tag the intent for each.
- Re-pull fan-outs for each and fill field 2.
- List cited URLs and mark the gaps in field 3.
- Set the format from the intent table.
- Write the brief, publish, and track the prompts.
- Check back in a month. Underperformers go back into the queue with a new brief.
For broader research on how AI engines pick sources, the Promptwatch Data hub is free and updated regularly. Fan-out and citation behavior will keep shifting, which is exactly why the template tells you to re-pull the data each time instead of trusting last quarter's spreadsheet.