There’s a trap a lot of companies fall into when it comes to measuring AI research, where they pick a set of broadly category claims, run them through ChatGPT or Perplexity a few times, and take the results as informative reading about their AI vision.
not so.
If your Quick Library overrepresents generic discovery queries, ignores different product lines, skips local competitors, or only tracks branded searches, your dashboard can appear comprehensive while pointing you in the completely wrong direction.
A really useful Instant Library is a curated sample of the AI-powered journeys that matter most to your business. Not every question is possible. It is not a list of keywords expanded into a sentence form. A representative set of how your customers actually think, compare and make decisions, with all the context they bring to it.
What makes a library rep fast?
The starting point is not writing prompts. It defines what the library needs to represent.
This means mapping your company across five dimensions before writing a single claim:
- Customer journey stage: Are you measuring discovery, evaluation, comparison, validation or transaction? Each stage produces different AI outputs and different insights.
- Product or Service Line: Multi-product businesses need separate claim sets for each offering. Topics, competitors and decision criteria can be very different.
- Audience or persona: A freelancer and an enterprise buyer ask different questions, use different language, and need different evidence before committing.
- Market and language: Local competitors, local sources, local regulations, and local signals of trust can change what an AI platform shows. Translating your UK applications into French does not make them representative of France.
- Business Priority: Not all of the above have equal business weight at the moment. The library should reflect where you really need to improve visibility.
The most important libraries that urgent libraries are missing: Buyer restrictions
Real AI research claims are not clean and generic. They are shaped by context. budget. Team size. industry. Tools that a person already uses. Compliance requirements. urgency.
“Best project management software” and “best project management software for a 20-person marketing agency that needs a client approval workflow and Slack integration” are two very different things. The second option is more likely to resemble how an actual buyer phrases their question and will yield more useful data.
Placing limitations in your claims means you’re measuring the AI’s insight into the decision contexts that actually matter, not a sanitized version of your market that doesn’t entirely exist.
How many claims do you really need?
Quite a few companies create hot libraries that are either too small to be meaningful or too large to maintain or act on. A reasonable starting point depends on the complexity of the business:
- One product, limited audience: 30-60 claims
- Multi-product or strong personal segmentation: 100-250 claims
- Enterprise, multi-country, multi-brand: Over 250 claims, organized by market, product line, and stage of the journey
But size is not the point. A small, well-organized library trumps a large, haphazard library every time. The goal is to recognize patterns, not size.
Where do you get your claims?
Don’t start from your own assumptions about how customers ask questions. Extract from sources that reflect real behavior:
- Demand data for search without branding
- Long queries from Google Search Console that perform poorly on clicks
- Sales call notes and CRM records
- Support tickets and live chat logs
- Reviews and community language (Reddit, industry Slack groups, forums)
- People also ask for data
The language your customers use when they’re feeling frustrated, comparing options, or looking for reassurance is far more useful than your internal description of what you do.
Keep platform results separate
There’s one last thing that’s often overlooked: don’t mix results across platforms.
Your brand may be recommended in ChatGPT, but absent from Perplexity, and misdescribed in Google’s AI mode. If you average those scores into a single “AI Vision Score,” you have hidden the specific insight that will actually tell you what to fix.
Track each platform individually. Reported separately. The differences are where the useful information is.
Next, we’ll look at how to use quick library results to diagnose what’s actually holding back your AI vision and how to prioritize fixes that will have the greatest business impact.
