How AI Recommendations Work Explained | Dabudai
How AI recommendations work: scenarios → sources → why competitors win
Kyrylo Poltavets
Jan 27, 2026
10-12 min min read
Key takeaways
- Track recommendations by scenario, not by brand-name prompts.
- Win sources: strengthen your site pages and your third-party coverage.
- Make your positioning simple. Add proof AI can cite.
- Publish “best for”, “vs”, and “alternatives” content with criteria and trade-offs.
- Measure weekly: visibility, share of voice, recommendation rate, and citation coverage.
The 3 outputs AI can give you
AI can treat your brand in three ways.
Track all three. They are not the same.
- Mention
- Citation
- Recommendation
Mention
Definition: AI includes your brand name in the answer.
How to spot it: your brand name appears.
How to measure it: count mentions across your scenario set.
What “good” looks like: you appear in the top options for your buyer scenarios.
Citation
Definition: AI uses your pages as a source for a claim.
How to spot it: your domain or page is referenced.
How to measure it: track citation / source coverage across scenarios.
What “good” looks like: AI uses your pages to justify key points about you.
Recommendation
Definition: AI tells the user to choose your brand for a specific need.
How to spot it: phrases like “choose”, “best for”, “I recommend”, “pick X if…”.
How to measure it: track recommendation rate across scenarios.
What “good” looks like: you are suggested for the right ICP and constraints.
Scenarios: why the question changes the answer
The question is not noise. It is the main input.
Scenario = intent + constraints
A scenario is a repeatable buyer question.
It has a fixed role, intent, and constraints.
How to define a scenario (step by step)
- Pick a buyer role (your ICP).
- Pick an intent: best, vs, alternatives, how-to, or definition.
- Add constraints: budget, region, team size, must-have features.
- Define success with a fixed format: top-3, criteria, trade-offs, and sources.
| Scenario type | Example prompt (dentistry) | Output focus | What you measure | Best content to improve it |
|---|---|---|---|---|
| Definition | “What is a dental implant? Explain simply. Who is it for?” | Citation | Mentions + citations of your site/pages | Glossary pages (Implant, Crown, Root Canal) |
| Best option | “Best dental clinic in [City] for implants. Give top 3 and why.” | Recommendation | Recommendation rate + list position | “Best for” landing pages + city pages |
| Vs | “Dental implants vs bridge: which is better for a missing tooth?” | Recommendation | Which option AI recommends + reasons | Comparison pages with criteria + FAQ |
| Alternatives | “Alternatives to braces for adults. What are options?” | Mention | Inclusion in option lists | Treatment options hub + guides |
| How-to | “How does a root canal work? Steps, pain, recovery time.” | Citation | Citation coverage + step quality | Step-by-step procedure pages + recovery guides |
How AI builds an answer (simple flow)
Most answer engines follow a similar flow.
The 5-step flow
Step 1: Detect intent
AI decides if the user wants a definition, a comparison, or a recommendation.
What you can do: create pages for “best / vs / alternatives”, not only generic posts.Step 2: Collect candidates
AI looks for information it can use.
What you can do: build strong first-party pages and grow consistent third-party coverage.Step 3: Select what to trust
AI favors content that is clear, consistent, and supported by proof.
What you can do: publish proof pages and improve page structure on key pages.Step 4: Synthesize an answer
AI combines the selected information into a single response.
What you can do: write short, direct statements and repeat the same wording across key pages.Step 5: Output a pattern
The answer becomes a mention, a citation, or a recommendation.
What you can do: track all three outputs across a stable scenario set.
A key point: the same brand can look different across prompts.
The source layer (what AI pulls from)
Your homepage is not enough. AI pulls from many sources.
First-party sources (your site)
These are pages you control. They should be clear, structured, and consistent.
Core pages (must-have):
- Homepage (what you do + best for)
- About (who you are + credibility)
- Pricing or Plans (constraints, limits, clarity)
Citable assets (high impact):
- Glossary pages (definitions AI can reuse)
- Methodology pages (how you measure or do the work)
- Proof pages (case studies, results, policies)
- Comparison pages (criteria + trade-offs + best for)
Third-party sources
AI also uses what others say about you.
Start with these (in this order):
- Reviews and ratings (highest trust signal)
- Directories and listings (easy visibility wins)
- Partner pages and industry guides (strong credibility)
What makes a source usable (signals checklist)
| Good signals: | |||
|---|---|---|---|
| 1. Clear topic and clear brand/entity name | |||
| 2. Specific claims with visible proof | |||
| 3. Strong structure: headings, lists, FAQ, tables | |||
| 4. Consistent wording across pages and sources | |||
| 5. Updated signals when freshness matters (dates, “last updated”, clear ownership) |
Bad signals:
- Vague claims (“best”, “leading”) with no proof
- Long blocks with no headings
- Conflicting positioning across pages
- Outdated or unclear pages
Why competitors win (7 common drivers) - improved, compact
This section explains how ai decides which brand to recommend — and why the same prompt can produce a different winner when sources and proof change.
The 7 drivers
- Clear category + “best for”
- Proof pages that can be cited
- Strong comparisons
- Consistent third-party coverage
- Better page structure
- Reputation signals
- Better scenario fit
Competitor analysis in 60 seconds
Use this method first.
- Pick 10 buyer questions. Use best / vs / alternatives.
- Run them in 2–3 answer engines.
- Write down who gets recommended for each question.
- Write down which sources are cited or referenced.
- Tag the winner’s main advantage: clarity / proof / coverage / structure / match.
Video: What is Retrieval-Augmented Generation (RAG)?
RAG (Retrieval-Augmented Generation) is one of the main reasons AI answers can include specific facts and sources.
Why this matters for AI recommendations:
- AI can only cite and recommend what it can reliably retrieve. If your product pages, docs, and proof points aren’t easy to find and extract, you’ll lose visibility to competitors.
Next steps (make this useful right now)
- Run the Clarity / Proof / Match check on 10 buyer questions.
- Set up weekly tracking with a fixed list of buyer questions.