How to Fix Wrong-Query Targeting

Your AI Visibility Tracker reports decent citation share for prompts you ARE tracking — but those may not be the prompts your real customers ask AI engines. The disconnect is structural: AI prompts are conversational and intent-rich, while traditional keyword research surfaces fragmented Google queries. This guide covers the prompt-research workflow and how to align content with how users actually phrase AI questions.

1. How AI prompts differ

Same intent, two phrasings:

Google query:    "best CRM small business"
AI prompt:       "I run a 15-person consultancy in London with mostly
                  inbound leads and no IT team. We use Gmail and Slack.
                  What CRM should I look at first and why?"

The AI prompt embeds context (size, location, lead source, tech stack) the user wouldn't type into Google. Content that answers the AI prompt fully — addressing each contextual element — wins citation. Content optimised for the Google keyword may miss entirely.

2. Where to find real AI prompts

Source 1: Customer interviews

5-10 customer interviews, one question each:
  "If you were to ask ChatGPT or Claude about [your category],
   how would you phrase it? Show me your actual prompt."

Don't accept abstractions. Get the literal text they'd type.
Patterns emerge after 5 interviews.

Source 2: Support ticket mining

Read 100 recent support tickets. Look for:
  - Questions phrased in user's own words
  - Context they include (size, role, tech stack)
  - Multi-part questions

These are your AI-prompt corpus. Real users, real questions, 
already in their voice.

Source 3: Reddit / forum mining

Reddit threads in r/[your-category] where users post:
  "I asked ChatGPT about X and got [response]"
  
These contain the prompts users actually use. Search:
  site:reddit.com "I asked ChatGPT" [your category]
  site:reddit.com "I asked Claude" [your category]
  site:reddit.com "Perplexity said" [your category]

Source 4: Your own AI chat logs

If you operate any AI chatbot (support, sales, demo):
  - Anonymised conversation logs are pure prompt data
  - User intent is explicit in their first message
  - Patterns of multi-turn refinement show what they really want

GDPR caveat: ensure user consent and anonymisation before mining.

Source 5: Public share-link aggregators

ChatGPT share links (chat.openai.com/share/...) get scraped 
and aggregated. Some tools (third-party AEO tools) provide 
prompt datasets. Verify quality before relying — public 
share-links skew technical and demo-heavy.

3. Build a target prompt set

20-50 prompts covering your category. Categorise:

Intent type            Example
─────────────────────────────────────────────────────────────
Educational            "How does X work?"
Comparison             "X vs Y, which is better for [context]?"
Recommendation         "I have [context], what should I use?"
Troubleshooting        "Why is [problem] happening with my X?"
Decision support       "Should I do X or Y given [context]?"
Definition             "What is X?"
Pricing                "How much does X cost for [context]?"
Implementation         "How do I set up X?"

Aim for balanced coverage. Over-weighting one intent type means missing customer segments at other journey stages.

4. Align content with prompt patterns

Content that answers a 30-word AI prompt completely outperforms content optimised for the 3-word Google query. Restructure:

Add context-rich sections

For prompts mentioning "small team" / "B2B" / "[region]":
  Add an explicit "For small B2B teams in [region]..." section.
  AI engines match prompt context to page context.

Example: "Recommendations vary by team size. For 5-15 person 
B2B teams in the UK, the top three options are..."

Build comparison content

"X vs Y" prompts dominate decision-stage queries.
Don't just have a page about X; have explicit X-vs-Y pages
that AI engines extract for comparison answers.

URL pattern: /compare/x-vs-y/
Schema: include both as mentioned entities

Address situational prompts

"I have [situation], what should I do?" prompts need
content that mirrors the situation.

Pattern:
  <h2>If you're a [situation], start here</h2>
  <p>For [situation], we recommend [specific path because 
     reasons that match the situation's constraints]...</p>

AI engines match this exact pattern to user prompts.

5. Map prompts to pages

For each target prompt, identify the page that should win the citation:

prompt: "I have a 12-person sales team selling B2B SaaS in 
         the UK, what CRM should I look at first?"
target page: /guides/crm-uk-b2b-small-team/
current content match: 60%
gaps:
  - Doesn't mention UK explicitly (add)
  - "12-person" range not addressed (add 10-15 person section)
  - "B2B SaaS" segment generic (add segment-specific recommendation)
priority: HIGH — high-intent prompt, content nearly there

Some prompts will need new pages. Some will need expansion of existing pages. Some have no relevant page at all — those are content opportunities.

6. Track prompt-level performance

The AI Visibility Tracker can take your custom prompt set. Run weekly:

Per prompt:
  - Cited? (your domain in response)
  - Mentioned? (your brand name without citation)
  - Recommended? (your brand recommended over alternatives)
  - Per engine: ChatGPT, Claude, Perplexity, Gemini, Copilot

Trend monthly:
  - Per-prompt citation share over time
  - Which engines newly cite vs lose citation
  - Which prompts moved from "invisible" to "cited"

Quarterly review:
  - Which content changes drove which prompt-level lifts
  - Where to invest next quarter