How LLMs Choose Which Brands to Recommend

When someone asks ChatGPT, Perplexity, or Claude "What's the best running shoe for beginners?" or "Which CRM works best for small businesses?"—an algorithm decides which brands to surface. Understanding how that selection process works is essential for any ecommerce brand that wants to appear in AI-generated recommendations.

Unlike traditional search engines that rank pages, LLMs synthesize answers from multiple sources, choosing which brands to mention based on a complex mix of training data, real-time retrieval, and trust signals. This article breaks down exactly how that process works—and what it means for your brand visibility strategy.

What Determines LLM Brand Recommendations?

LLMs don't "pick brands" the way a search engine ranks pages. Instead, they infer which brands to surface based on a combination of training data exposure, real-time signals from web searches, and product-level business rules—all filtered through safety and relevance constraints.

The selection process involves three main layers: knowledge embedded during training, information retrieved in real-time, and filtering based on policies and user context. Each layer contributes to which brands ultimately appear in the response.

The Training Data Layer

During pretraining, LLMs ingest massive corpora of web content: news articles, product reviews, forums, social media, how-to guides, comparison articles, and "best X" lists. Through this process, models implicitly learn which brands are frequently mentioned in positive, authoritative contexts.

Brands that appear repeatedly in trusted sources—co-mentioned with industry experts, standards bodies, or regulators—build stronger associations in the model's knowledge. The model learns patterns like "When users ask about [category], these brands are commonly recommended."

This is why historical digital presence matters. If your brand has been featured in industry publications, respected comparison sites, and expert roundups before the model's training cutoff, you're more likely to be recognized as a relevant entity in your category.

The Real-Time Retrieval Layer

Most production LLM systems in 2026 use retrieval-augmented generation (RAG) or integrated search tools. When you ask for "best email marketing platforms," the model doesn't rely solely on training data—it performs a live web search to supplement its knowledge.

The retrieval process works in stages: First, the model calls a search or knowledge tool. That tool returns results ranked by its own algorithms—traditional SEO signals, review aggregations, marketplace rankings. Then the LLM filters these for relevance, summarizes them, and sometimes balances across categories.

For ChatGPT specifically, this retrieval happens primarily through Bing's index. Brands well-optimized for Bing—not just Google—gain an advantage in being discovered and recommended. This is a significant shift for teams focused exclusively on Google SEO.

Analyses of ChatGPT's cited sources show strong recency bias: the majority of citations come from content published in 2023-2025. Actively maintained comparison pages, product guides, and case studies are more likely to appear in retrieved evidence.

Trust Signals LLMs Recognize

Beyond simple mention frequency, LLMs evaluate several trust signals when deciding which brands to recommend. These signals help the model determine whether it's "safe" to suggest a brand—whether the recommendation is likely to be helpful rather than misleading.

Consistency Across Sources

Brands whose claims match across documentation, press coverage, help centers, partner sites, and user reviews generate fewer contradictions for the model. Consistent messaging across platforms increases confidence that information is reliable and worth citing.

Evidence-Rich Content

Pages with data, benchmarks, third-party studies, and clear specifications are easier for models to quote and cross-check than vague marketing copy. LLMs favor factual, data-driven, technically detailed content over promotional language.

Third-Party Validation

Awards and accreditations from recognized institutions contribute approximately 18% of recommendation influence. Online reviews and testimonials on platforms like G2, Capterra, or Trustpilot provide another 16% of influence. First-hand user stories and case studies on high-authority sites carry significant weight.

Clear Entity Structure

Consistent brand and product names, structured specifications, and unambiguous positioning help the model recognize your brand as a stable entity across the corpus. When the model can clearly understand what category you belong to and what you're known for, it's more likely to include you in relevant recommendations.

Safety and Filtering Constraints

Modern LLM systems apply policy and safety filters before recommending brands. These constraints significantly affect which brands appear—and which get filtered out.

Common constraints include: avoiding explicit endorsements in regulated categories (medical, financial), balancing mentions across multiple options rather than picking a single winner, and excluding brands flagged for misinformation or policy violations.

Enterprise deployments often add additional layers: allowlists and blocklists of approved vendors, industry or region restrictions (only GDPR-compliant EU vendors, for example), and partner prioritization where contractually permitted.

Why Traditional SEO Isn't Enough

Traditional SEO signals—backlinks, keyword optimization, domain authority—have minimal direct influence on which brands LLMs name in recommendations. The tactics that worked for search engine rankings don't directly map to LLM visibility.

The fundamental difference: LLMs don't display rankings or snippets. They synthesize answers. Keyword density and generic backlinks matter less than high-quality, deeply informative content, machine-readable structure, and being cited by other high-authority sources.

SEO still matters indirectly because it helps content rank and be discovered by search engines (including Bing), which feeds the ecosystem from which LLMs retrieve evidence. But optimizing for LLM recommendations requires a different focus: GEO (Generative Engine Optimization).

What This Means for Your Brand

Understanding how LLMs select brands points to specific optimization strategies:

Get featured in authoritative lists: Since list mentions drive 41% of recommendations, pursue coverage in "best of" roundups, industry comparisons, and expert guides on reputable sites. This matters more than most other tactics.

Build third-party validation: Accumulate reviews on platforms LLMs trust (G2, Capterra, Trustpilot). Pursue industry awards and certifications. Get mentioned in analyst coverage and case studies on third-party sites.

Create evidence-rich content: Publish data, benchmarks, technical specifications, and detailed comparisons. Original research and proprietary data are especially valuable because LLMs can cite unique information they can't synthesize from elsewhere.

Maintain consistency: Ensure your brand messaging, feature descriptions, pricing, and positioning align across your website, documentation, social media, and third-party mentions. Contradictions reduce model confidence.

Track your visibility: Use tools like alicerank to monitor which prompts surface your brand, how you're positioned relative to competitors, and whether your optimization efforts are working.

For detailed implementation guidance, see our articles on content optimization for AI citations, E-E-A-T signals for AI visibility, and understanding the shift from rankings to citations.

Sources

• Onely: How ChatGPT Decides Which Brands to Recommend
• Contently: The Emerging Signals LLMs Use to Trust Your Brand
• Brandon Leuangpaseuth: LLM Ranking Factors