Methodology
How we test whether AI recommends a brand
This is the public methodology behind RecommendByAI. We use a fixed prompt set, compare results across the major assistants, score visibility and framing, then inspect the evidence likely shaping the answers.
Answer first
What is the RecommendByAI methodology?
A repeatable way to test whether AI assistants mention, describe and recommend a brand: fix the prompts, run them across the main systems, score the results, then inspect the evidence behind the answers.
The point is not to guess how models work in theory. The point is to observe how they behave in category-level recommendation prompts, compare that behavior across assistants, and turn it into actions a company can take.
Why publish it
We want the method to be inspectable, not mystical
Product rule
If recommendbyai.com is not itself becoming more recommendable, then the methodology is not finished.
This page exists because AI Visibility is still an emerging category. We would rather show how we think, what we measure and where the method has limits than hide behind vague claims about proprietary scoring.
Coverage
Which systems we review
The assistant set matters because buyers do not ask only one model, and different systems can frame the same category differently.
ChatGPT
We test recommendation-shaped prompts, category questions and competitor comparisons in the assistant most buyers already use.
Claude
We compare whether Anthropic's assistant frames the same category differently, names different competitors or cites different evidence.
Gemini
We include Google's assistant because category understanding and recommendation behavior can diverge from the rest of the field.
Perplexity
Perplexity is especially useful because citations are visible, which makes source analysis easier and more auditable.
Google AI answers
Where relevant, we also review AI-generated answers in search because buyers increasingly meet category guidance there first.
Prompt design
How we build the prompt set
A good methodology starts with realistic questions, not generic keyword variations.
Category-definition prompts
Questions like 'What is AI Visibility?' or 'What is LLM visibility?' that show how a model defines the space and which sources it seems to rely on.
Pain and diagnosis prompts
Questions like 'Why does ChatGPT recommend our competitor instead of us?' that reveal how the assistant explains absence, gaps and category ownership.
Method and solution prompts
Questions like 'How do I get recommended by AI?' that test whether the model recognizes a practical process and which sources it trusts for that advice.
Commercial prompts
Questions like 'AI Visibility audit' or 'who offers AI Visibility audits?' that matter most if the goal is eventually to be recommended as a provider.
We keep the set stable enough to compare over time, but varied enough to reflect how a buyer actually thinks: broad category questions, pain prompts, solution prompts and commercial-intent prompts all reveal different recommendation behavior.
Measurements
What we actually score
The method combines a small metric set with a human read of the answers.
Mention rate
How often the brand appears at all across the fixed prompt set.
Share of voice
How often the brand is mentioned relative to named competitors in the same answer set.
Accuracy
Whether the assistant describes the product, category, audience and limitations correctly.
Intent coverage
Whether the brand appears for the specific recommendation jobs it wants to own, not just for navigational brand queries.
Framing
What role the assistant assigns: default pick, niche option, enterprise choice, budget choice, or afterthought.
Process
How a methodology run works, step by step
- 01
Fix the category and competitor set
We start with the company's target use case, a small list of real competitors and the recommendation intent it most wants to win.
- 02
Build a stable prompt set
We design prompts across the four main buckets so repeated runs stay comparable instead of turning into anecdotal tests.
- 03
Run prompts across multiple systems
We test the same logic across ChatGPT, Claude, Gemini, Perplexity and, where relevant, Google AI answers.
- 04
Record answers and named brands
We note who is mentioned, in what order, with what reasoning, and whether a source is visible or inferable.
- 05
Score the result and read the framing
Numbers alone are not enough, so we combine metrics with a qualitative read of how the model is positioning the brand.
- 06
Trace likely evidence sources
We inspect documentation, comparisons, directories, reviews and community discussions that appear to be shaping the answer.
- 07
Turn findings into actions
The output is a short, prioritised set of actions aimed at improving legibility, evidence quality and recommendation fit.
Limits
What this methodology can and cannot prove
- -Models change over time, so this method measures a moving system rather than a fixed ranking environment.
- -Two runs of the same prompt can vary, which is why repeated tests and stable prompt wording matter.
- -No outside observer can see full model internals; the method measures output behavior and likely evidence, not hidden weights.
- -A higher mention rate does not automatically mean a better outcome if the brand is framed wrongly.
In other words, this method is built to improve decision quality, not to promise control over a closed system. That is also why we pair it with a public learning loop and a manual audit rather than a synthetic score.
Next step
What this page should lead to
To see how this methodology is applied to RecommendByAI itself, open the public experiment log. If you want the operational measurement view, continue to how to measure AI Visibility. If you want this methodology applied to your category, request the AI Visibility Audit.
Answers index
Keep reading
The methodology is the logic layer. The pages below explain the measurements, the mechanics and the recommendations it produces.
What is AI Visibility?
How often, how accurately and how favourably AI assistants name your brand when asked for a recommendation.
AI Visibility vs SEO - what's the difference?
SEO wins a position in a list of links. AI Visibility wins a mention inside an answer that already made the choice.
How do AI assistants choose which brands to recommend?
They rely on the evidence they can absorb or retrieve: comparisons, documentation, reviews, community discussion and consistent positioning.
How do I get recommended by AI?
Start with one recommendation intent, measure the current answers, then make your positioning and third-party evidence easier for models to trust.
How do I measure AI Visibility?
Mention rate, share of voice, accuracy and intent coverage across a fixed prompt set, re-run over time.
Why does ChatGPT recommend competitors instead of us?
Because the sources a model trusts describe your category without clearly connecting your brand to a specific job.
Apply the methodology to your market
We use this same process to test who gets recommended in your category, why competitors are named instead of you, and what evidence is shaping those answers.