AI Recommendation (LLMO)
Get recommended when people ask ChatGPT or Gemini. Define customer questions (CEP), find the AI's selection criteria (KBF), and deliver your reasons to believe (RTB) to articles and outside media
Getting picked as "recommended" by AI
Ranking high in search results is not enough if ChatGPT or Gemini leave your name out when someone asks "which one do you recommend?" "Sorabun > Leave It to AI > AI Recommendation (LLMO)" walks you through four steps to get chosen by AI on one screen (Pro, administrators only).
| Step | What you do |
|---|---|
| 1. Design the questions (CEP) | Decide what customers ask AI (their situation, problem and conditions) |
| 2. Identify the criteria (KBF) | Actually ask AI and find out what it uses to compare candidates |
| 3. Organize the evidence (RTB) | For each AI criterion, check whether you show facts and data that make you the right choice |
| 4. Place it where AI looks | Deliver missing evidence to your own site (articles) and to outside media that AI reads |
AI builds answers from two sources: pre-training (learned knowledge and mentions on the web) and retrieval (RAG) (outside information, comparison media and official sites it searches while answering). This screen shows the steps to get your evidence into both.
1. Design the questions
- Your name: when this name (or one of its aliases) appears in an AI answer, it counts as "recommended". The name is never included when asking AI (that would skew the answer and make the measurement meaningless)
- Customer questions: one per line, up to 30. Write them the way people talk to AI, not as search keywords. Include plenty of questions where AI lists candidates, like "which one do you recommend?"
- "Have AI write questions": creates about 15 questions from your Site Diagnosis and Rank Tracking keywords. Edit them closer to your real customers' words before saving
2. Ask AI and find the selection criteria
Press "Ask AI and measure" to ask each question to Gemini (with Google Search) and ChatGPT (with web search), one at a time. From each answer it reads:
- The companies and services named as recommendations (in order), and where you appear among them
- The criteria used to compare candidates (fees, support, track record and so on)
- The sites used as sources
You then see the share of answers that named you (overall and per AI), your average position when named, and each question's answer (you can open the full text). Every measurement is added to the history.
One measurement makes "questions × AIs" queries, the same number of reads, plus one final analysis (about 61 calls for 15 questions × 2 AIs). Costs follow your API key pricing, and queries with search cost more than usual. "Re-measure once a week with the same questions" is off by default.
It goes one pair at a time, so it takes a few minutes. Even if you close the screen, it continues every hour. AI answers vary a little each time, so look at the trend rather than a single result.
3. Evidence for each selection criterion
The collected criteria are grouped into 4 to 8 "AI selection criteria", and your evidence is checked for each. Evidence comes from the Evidence Lab, first-party data, Site Diagnosis and published articles.
| Result | Meaning |
|---|---|
| Evidence | Shown with facts, numbers or third-party reviews |
| Weak evidence | Mentioned, but without numbers or sources |
| No evidence | Your site does not show why you would be chosen on this criterion |
"Evidence to publish" tells you what to make public. Making up evidence is never recommended. Register real numbers, cases and third-party reviews in the Evidence Lab with "Register evidence", then turn them into an article with "Write an evidence article".
4. Place it where AI looks
- Media AI cites: sites AI reads while answering. Aim to get your evidence onto them through listings in comparison articles, interviews, guest posts or reviews
- Others AI recommended: how many times each was named. Looking at what evidence they publish for the criteria above shows what you are missing
- Your own site: set evidence articles to the "LLMO" or "Hybrid" optimization type, and turn on llms.txt and structured data (JSON-LD) (SEO Plugin Integration and Structured Data)
Used together with AI citation check (whether your site is among the sources for each keyword), you can see both "are you read as a source" and "are you named as a recommendation".