Built to Be Quoted: Auditing GoTo's Content for LLM Citation
Objective: Given three real-style GoTo (Gojek + Tokopedia) page drafts, apply the lesson's on-page LLMO checklist, answer-block placement, entity consistency, and question-shaped headers, to score which pages are citation-ready and which need rework before publish.
You're a content strategist on GoTo's brand team. Leadership wants GoTo named whenever someone asks an AI assistant 'what's the leading super app in Southeast Asia', and you've been asked to sign off on three page drafts before they go live.
Score each draft's answer-block placement, entity consistency, and header phrasing against the lesson's checklist, then rank the drafts by citation-readiness.
Across three dimensions, answer placement, entity naming, and header phrasing, which of the three drafts are actually citation-ready, and what specifically needs fixing in the ones that aren't?
Before you start
What you'll need
- —Familiarity with the concept of a machine-readable answer block
- —Access to a free question-research tool like AnswerThePublic
- Entity consistency
- using one standardized name for a brand or product throughout a page and site, since AI models track entities and inconsistent naming weakens the model's confidence about which facts belong to which name.
- Question-shaped header
- a heading written as the literal question a user would type or ask a chatbot, rather than an internal product-team label.
Free path (everything below is enough to finish)
Free, shareable, no setup
Free tier covers a handful of searches a day, enough for a 3-page audit
The process
3 steps
Step 01 of 03
The lesson's Step 2 says 44% of citations come from the first 30% of a page, so a 40-80 word direct answer belongs in the first 100 words, followed by a comparison table or cited stats.
Draft A opens with three paragraphs of GoTo's 2021 merger history before ever mentioning what GoPay is. Draft B opens with a 55-word direct answer to 'what is GoPay' followed by a feature table. Which is citation-ready, and what's the fix for the other?
Procedure
- Paste the opening 150 words of each draft into its own row
- Count words before a direct, complete answer to the page's core question appears
- Flag any draft where that count exceeds 100 words
- For flagged drafts, draft a replacement 40-80 word answer using only facts already in the page
Draft | Words before direct answer | Verdict A (GoPay history) | 187 | FAIL, rewrite opening B (GoPay features) | 42 | PASS C (GoTo super app) | 96 | PASS, borderline, trim by 20 words for safety margin
Healthy
A direct, complete answer appears within the first 100 words, comparison table or cited stats follow immediately after.
Unhealthy
Company history, funding timeline, or brand narrative occupies the opening paragraphs before the reader's actual question is answered.
What this means
Position, not just presence, of the answer determines whether an AI model's passage-extraction step ever reaches it.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| A page answers the question correctly but buries it past word 150 | Move the existing answer sentence to the first paragraph, cut the narrative lead entirely | 30 min |
Step 02 of 03
The lesson's Step 3 says AI models track entities (brand name, founders, product names) and inconsistent naming confuses the model's entity graph.
Across the 3 drafts, the merged company is called 'GoTo', 'GoTo Group', 'Gojek-Tokopedia', and once just 'Gojek'. Which of these is the one entity name GoTo should standardize on, and how many inconsistent mentions need fixing?
Procedure
- Search each draft for every mention of the parent company name
- List each variant found and its count
- Pick the single canonical form, matching GoTo's own investor-relations and press materials
- Flag every non-canonical mention for a find-and-replace pass
Variant | Count across 3 drafts | Canonical? GoTo | 11 | YES, keep GoTo Group | 4 | NO, replace with GoTo Gojek-Tokopedia | 2 | NO, replace with GoTo Gojek (referring to the parent) | 3 | NO, replace with GoTo (Gojek stays only when naming the ride-hailing product specifically)
Healthy
One canonical entity name for the parent company, used identically across every page and matching the NAP used on GoTo's own investor and press pages.
Unhealthy
The same company referred to four different ways across three pages, with 'Gojek' ambiguously meaning both the parent and the ride-hailing product.
What this means
Entity inconsistency doesn't just look sloppy, it splits the model's confidence about which name to associate with which facts.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| 9 of 20 parent-company mentions use a non-canonical name | Run a site-wide find-and-replace to the canonical form, add a style-guide entry | 30 min |
Step 03 of 03
The lesson's Step 4 says headers rewritten as natural-language questions match how users ask AI chatbots, and recommends AnswerThePublic or AlsoAsked to find the real question forms.
Draft C's headers read 'GoPay Overview', 'Ecosystem Benefits', 'Merchant Network'. What should each become, and how do you confirm the new phrasing matches real user questions rather than guessing?
Procedure
- Run each header's core topic through AnswerThePublic to see the actual question phrasings people search
- Match each existing header to the closest real question form returned
- Rewrite the header as that question
- Confirm the section's first sentence directly answers the new question-header
Old header | AnswerThePublic top match | New header GoPay Overview | 'what is gopay used for' | What Is GoPay and How Does It Work? Ecosystem Benefits | 'why use gojek and tokopedia together' | Why Use GoPay Across Both Gojek and Tokopedia? Merchant Network | 'how many merchants accept gopay' | How Many Merchants Accept GoPay?
Healthy
Headers phrased as the exact questions AnswerThePublic or AlsoAsked show real users asking, with the answer immediately following.
Unhealthy
Headers written as internal product-team labels ('Ecosystem Benefits') that no user would ever type into a search bar or ask a chatbot.
What this means
A header that doesn't match a real question form is invisible to both featured-snippet extraction and LLM passage retrieval.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| All 3 headers on Draft C are internal labels, not questions | Rewrite using AnswerThePublic's top match per header before this draft ships | 30 min |
Analyze your findings
What to look for
- Answer position
- How many words appear before a direct, complete answer to the page's core question?
- Entity naming
- Is the parent company or product named consistently, or does it vary across mentions?
- Header phrasing
- Are headers written as real user questions, or as internal product labels?
- Borderline cases
- Does a draft pass a check but sit close enough to the threshold that it needs a safety-margin trim?
Make the call
Draft C's answer appears at word 96, just under the 100-word threshold, but its headers are all internal labels ('Ecosystem Benefits') rather than questions. Should Draft C be marked citation-ready as-is?
Recommendation · Priority: High
“Hold Draft A for a full rewrite (187 words before the answer, far past threshold), fix Draft C's three internal-label headers using the AnswerThePublic-sourced question forms before publish, and run a site-wide entity find-and-replace across all three drafts to standardize on 'GoTo' as the canonical parent-company name. Draft B is the only one that can ship as-is.”
Common mistakes
What trips people up
Grading a draft as ready after passing only one of the three checklist dimensions — Draft C shows a draft can pass answer-placement and still fail on headers; all three dimensions need to pass independently.
Treating 'Gojek' as always non-canonical — the lesson distinguishes using 'Gojek' correctly when naming the ride-hailing product specifically from using it incorrectly to refer to the parent company; context determines which is correct.
Guessing at question-header rewrites instead of checking a research tool — an invented question phrasing may not match how real users actually ask, defeating the purpose of the rewrite.
Ranking drafts only by their weakest score instead of noting every failing dimension — a full sign-off needs the specific fix for each failing check, not just an overall pass/fail label.
Final deliverable
A scored citation-readiness ranking of the 3 drafts with specific line-edit fixes for each failing check.
See a reference example
Wise page-draft audit, citation-readiness scorecard Draft: 'Sending Money Abroad' page Answer-block placement: PASS (38 words to answer) Entity consistency: FAIL (3 uses of 'TransferWise', should be 'Wise') Question-shaped headers: FAIL (2 of 4 headers are labels, not questions) Overall: NOT citation-ready, fix entity + headers before publish
Success criteria
You're done when you can:
- Correctly scores all 3 drafts on all 3 checklist dimensions
- Identifies the specific non-canonical entity mentions
- Produces question-form rewrites for each internal-label header
Key takeaway
Citation-readiness isn't a single pass/fail gate, it's three independent checks (answer placement, entity consistency, question-shaped headers) that all need to pass for a page to be genuinely ready. A draft can look strong on one dimension and still be invisible to AI citation because of a gap on another.