How LLMs Decide What to Recommend: Inside the Black Box

Introduction

When a user asks ChatGPT “What is the best CRM for a small business?”, the response feels instantaneous and authoritative. Three platforms are recommended with specific reasons why each suits different needs. The user never questions the source of this confidence. They trust the AI’s judgment.

But behind that seemingly simple answer lies a complex series of decisions, calculations, and trade-offs. The AI isn’t pulling from a curated database of “approved recommendations.” It’s generating each word based on probability distributions, training data, real-time retrieved information, and algorithmic constraints designed to balance helpfulness with accuracy.

Understanding these mechanisms is the foundation of effective Generative Engine Optimization (GEO). If you don’t understand how LLMs decide what to recommend, you can’t strategically optimize for inclusion. This article provides a technical deep dive into the decision-making processes that determine whether your brand appears in AI-generated recommendations or remains invisible.

The Architecture of AI Recommendations

Modern large language models (LLMs) like ChatGPT, Claude, Gemini, and Perplexity combine multiple systems to generate responses:

1. Pre-trained parametric knowledge (the model’s “memory” from training)
2. Retrieval-augmented generation (RAG) (real-time web search and document retrieval)
3. Ranking and selection algorithms (choosing which information to include)
4. Safety and quality filters (preventing harmful or low-quality outputs)
5. User context and personalization (adapting to conversation history and preferences)

Each of these layers influences whether your brand gets mentioned. Let’s examine each in detail.

Layer 1: The Training Data Foundation

What Training Data Actually Is

LLMs are trained on massive text corpora scraped from the internet, books, academic papers, Wikipedia, Reddit, GitHub, and countless other sources. For models like GPT-4 or Claude, this training data represents trillions of words captured at specific points in time.

Training cutoffs matter. A model with a knowledge cutoff of April 2023 has no parametric knowledge of products launched in June 2023, rebrands that occurred in 2024, or companies founded after the cutoff date. This information simply doesn’t exist in the model’s weights—its “memory.”

How Training Data Influences Recommendations

During training, the model learns statistical associations between concepts. If your brand appears frequently in authoritative contexts during the training period, the model develops strong associations:

High Training Visibility Example: If hundreds of articles in the training data mention “Salesforce is the leading enterprise CRM,” the model encodes this pattern. When prompted about CRM solutions, “Salesforce” has a high probability of being generated, especially for enterprise contexts.

Low Training Visibility Example: If your startup launched six months before the training cutoff and only had limited press coverage, the model has weak or no associations with your brand. Even if you now dominate your market, the model’s parametric knowledge is outdated.

The Volume-Authority Trade-Off

Training data influence isn’t purely about volume. A single mention in a highly authoritative source (like a peer-reviewed academic paper, major publication, or official documentation) can carry more weight than dozens of mentions in low-quality content.

Why this matters for GEO: Building strong training data presence requires consistent, authoritative coverage over time. This is a long-game strategy—influence training data that will be scraped for future model versions.

Knowledge Stale-ness Problem

Training data becomes stale. Models trained in 2023 don’t know about 2024 developments unless they use real-time retrieval. This creates a challenge: brands with strong historical presence have advantages, while newer brands or rebranded companies are invisible in parametric knowledge.

Solution: RAG systems partially address this by retrieving current information. But for queries where RAG isn’t triggered or when users ask general questions, parametric knowledge dominates.

Layer 2: Retrieval-Augmented Generation (RAG)

RAG systems revolutionized LLM capabilities by allowing models to fetch current information before generating responses. Understanding RAG is critical for GEO because it creates opportunities for real-time influence.

How RAG Works: A Step-by-Step Process

When a user submits a query to an AI system with RAG capabilities:

Step 1: Query Analysis The LLM analyzes the user’s question to determine if additional information is needed. Queries that are time-sensitive, specific, or outside the model’s training period trigger RAG.

Step 2: Search Query Generation The model generates optimized search queries to find relevant information. For example, if a user asks “What are the best marketing automation platforms in 2025?”, the model might generate searches like:

• “best marketing automation platforms 2025”
• “top marketing automation software”
• “marketing automation comparison 2025”

Step 3: Information Retrieval The RAG system searches the web (or a proprietary document database) and retrieves results. Different systems use different retrieval sources:

• Perplexity: Real-time web search with Bing or Google
• ChatGPT with browsing: Web search via Bing
• Claude with web access: Web retrieval (specific mechanism varies)
• Gemini: Google Search integration

Step 4: Content Processing Retrieved web pages are processed, cleaned, and chunked into digestible pieces. The system extracts key information: titles, summaries, facts, quotes, data points.

Step 5: Ranking and Selection Not all retrieved information is used. The system ranks sources based on:

• Relevance to the original query
• Authority of the source domain
• Recency of publication
• Content quality signals
• Consistency with other sources

Typically, only the top 5-20 results are used to inform the final answer.

Step 6: Synthesis The LLM reads the retrieved content and synthesizes it into a coherent response, often with citations linking back to source material.

Optimizing for RAG: The New SEO

Because RAG systems often retrieve from search engine results, traditional SEO still matters for GEO. If your content ranks in the top 10 Google or Bing results for relevant queries, you’re more likely to be retrieved and cited by RAG-powered AI.

Key differences from traditional SEO:

1. Citability matters more than click-through rate. Even if users don’t click your SERP listing, RAG systems will read and extract from it.
2. Content structure matters for extraction. Clear, factual statements are easier for LLMs to extract and cite than complex prose.
3. Authority signals are amplified. RAG systems preferentially retrieve from high-authority domains recognized by both search engines and LLMs.

RAG Limitations and Implications

RAG isn’t perfect:

Limitation 1: Query Triggers Not all queries trigger RAG. Simple questions that the model can answer from parametric knowledge alone may not initiate retrieval. This means training data still matters even in RAG-enabled systems.

Limitation 2: Retrieval Quality If the retrieval system doesn’t find your content (poor SEO, new site, low authority), you won’t be considered for inclusion—even if you’re the best answer.

Limitation 3: Synthesis Bias Even if your content is retrieved, the model still decides how to synthesize information. Content that is clearer, more authoritative, or more consistent with other sources is more likely to be featured prominently.

Layer 3: Authority and Citation Recognition

LLMs are trained to recognize and weight authoritative sources more heavily. This isn’t arbitrary—it’s a key safety and quality mechanism.

How LLMs Recognize Authority

Domain-Level Authority: LLMs learn which domains are generally trustworthy based on patterns in training data:

• Academic institutions (.edu)
• Government sites (.gov)
• Major publications (NYT, WSJ, The Economist)
• Industry-specific authorities (for tech: ArsTechnica, TechCrunch, etc.)

Citation Network Authority: Brands and sources that are frequently cited by other authoritative sources gain transitive authority. If The Wall Street Journal, Forbes, and TechCrunch all mention your company, the LLM learns to view your brand as significant.

Entity Recognition: LLMs recognize named entities (brands, products, people, organizations) and build understanding of their relationships and attributes. Strong entity presence across the web reinforces authority.

The Citation Advantage in Practice

Consider two hypothetical CRM providers:

Company A (Strong Citation Network):

• Mentioned in 50 authoritative publications
• Quoted executives in industry analysis pieces
• Referenced in case studies by major consulting firms
• Featured in academic research on SaaS business models
• Active Wikipedia presence with detailed article

Company B (Weak Citation Network):

• Mentioned primarily in paid directories and sponsored content
• Limited press coverage outside of company-authored blog posts
• Few third-party case studies or references
• No Wikipedia presence

When an LLM decides what to recommend, Company A has enormous advantages. The model has learned through thousands of training examples that Company A is a significant, trustworthy entity in the CRM space. Company B barely registers as a relevant option.

Building Citation Authority

This is where GEO diverges most from traditional SEO. You can’t simply build backlinks to manipulate PageRank. You need substantive citations that teach LLMs about your authority and expertise.

Effective citation-building strategies:

1. Earn press coverage that substantively explains your product, technology, or market position
2. Publish original research that other publications cite
3. Quote executives in industry analysis and trend pieces
4. Partner with authoritative organizations and have those partnerships documented
5. Build Wikipedia presence (following Wikipedia’s notability guidelines)
6. Sponsor or present at industry conferences that publish proceedings
7. Publish academic or technical papers if applicable to your industry

Layer 4: Consensus and Sentiment Analysis

LLMs don’t just count mentions—they analyze consensus and sentiment across sources.

How Consensus Influences Recommendations

When multiple authoritative sources agree, LLMs gain confidence. If 30 articles describe your product as “the best solution for small businesses,” the model learns this association strongly. If sources disagree or present mixed perspectives, the model hedges or omits your brand entirely.

Example: CRM Market Consensus

If training data and retrieved sources show:

• 80% of articles mention Salesforce for enterprise
• 70% mention HubSpot for mid-market
• 60% mention Zoho for small business and value

The LLM learns strong consensus around these positioning categories. When asked for recommendations, these brands will appear with high frequency because the model has high confidence in these associations.

Sentiment’s Role in Recommendations

Sentiment analysis identifies positive, neutral, and negative framing:

Positive Sentiment: “Salesforce revolutionized CRM with its cloud-first approach and remains the market leader.”

Neutral Sentiment: “Salesforce is a CRM platform with a large market share.”

Negative Sentiment: “Salesforce has faced criticism for its complex pricing and steep learning curve.”

LLMs trained on predominantly positive sentiment will recommend brands more enthusiastically. Mixed or negative sentiment leads to caveats, hedging, or omission.

Implications for Brand Management

You need to actively monitor and shape the sentiment of your brand’s mentions across the web. This isn’t about “spin”—it’s about ensuring accurate, positive information is more prevalent than outdated complaints or misconceptions.

Strategies:

1. Respond to and resolve public complaints and negative reviews
2. Publish updated case studies showing positive outcomes
3. Earn coverage that frames your brand positively and accurately
4. Address misconceptions proactively with clear, factual content
5. Build fresh positive citations to dilute the impact of older negative content

Layer 5: Recency and Temporal Awareness

LLMs understand temporal context. When users ask about “the best X in 2025,” the model prioritizes recent information over older content.

How Recency Is Weighted

RAG systems: Prefer content published recently (within the last 6-12 months for time-sensitive queries)

Training data: The model’s baseline knowledge reflects the time period when training occurred. More recent mentions in training data (closer to the cutoff date) are often more influential than older mentions.

Temporal decay: Over time, older information becomes less influential unless it’s consistently reinforced by new citations.

Strategies for Maintaining Temporal Relevance

1. Publish fresh content regularly (not just blog posts—press releases, case studies, documentation)
2. Update existing high-authority content with current information and republish dates
3. Earn regular press coverage to maintain fresh citation flow
4. Participate in annual reports and surveys that get published and cited
5. Release version updates and announcements that get covered by industry press

Layer 6: Entity Recognition and Knowledge Graphs

Modern LLMs leverage structured knowledge graphs to understand entities and their relationships. This layer is often underestimated but critically important.

What Are Knowledge Graphs?

Knowledge graphs are structured databases of entities (people, places, brands, concepts) and their relationships. Major knowledge graphs include:

• Wikipedia/Wikidata: The largest open knowledge graph
• Google Knowledge Graph: Powers Google search features
• Proprietary LLM knowledge bases: Internal structured data maintained by AI providers

How Knowledge Graphs Influence Recommendations

When an LLM encounters a brand name, it can query associated knowledge graph entities to retrieve:

• Official company name and aliases
• Product categories and offerings
• Founding date and history
• Key leadership and personnel
• Related companies and competitors
• Geographic presence
• Notable achievements or controversies

This structured information supplements the probabilistic text generation with factual grounding.

If your brand has a strong knowledge graph presence, LLMs can more accurately and confidently represent you. If you’re absent or poorly represented in knowledge graphs, the model may confuse you with competitors or omit you entirely.

Optimizing Knowledge Graph Presence

Critical actions:

1. Create and maintain a Wikipedia article (if you meet notability requirements)
2. Claim and optimize your Wikidata entity
3. Implement comprehensive Schema.org structured data on your website
4. Maintain consistent NAP (Name, Address, Phone) across the web
5. Register your brand in relevant industry directories and databases
6. Optimize Google Business Profile if applicable
7. Build structured data for products, executives, locations, and events

Layer 7: Safety and Quality Filters

LLMs implement multiple layers of safety and quality filtering that affect recommendations:

Content Policy Filters

Models are trained to avoid:

• Controversial or potentially harmful recommendations
• Brands associated with misinformation or unethical practices
• Products in regulated categories without appropriate disclaimers
• Sources that violate content policies

If your brand has negative associations (even if unfair or outdated), these filters may suppress mentions.

Quality Thresholds

Models implement quality thresholds to avoid citing:

• Low-quality or spammy content
• Websites with poor design or usability signals
• Content with grammatical errors or incoherent writing
• Sources that contradict established facts

Implication: Even if you rank well in search, poor content quality can lead to omission from AI responses.

Bias Mitigation

LLMs attempt to avoid:

• Bias toward particular brands or companies
• Favoritism based on commercial relationships
• Over-representation of sources from specific geographic regions or demographics

This means that diversifying your citation sources and building global, diverse mentions can improve representation.

The Integration: How All Layers Work Together

When a user asks “What is the best project management tool for a remote team of 15?”, here’s how the layers integrate:

Step 1: The model analyzes the query and recognizes that current, specific information is needed.

Step 2: RAG is triggered. The model generates search queries and retrieves top results from the web.

Step 3: Retrieved sources are ranked based on authority, recency, and relevance. Top sources might include:

• A 2025 comparison article from a reputable tech publication
• A detailed review from a software review site
• A recent user survey or report

Step 4: The model reads and extracts key information, identifying brands mentioned frequently across sources: Asana, Monday.com, ClickUp, Notion.

Step 5: The model cross-references parametric knowledge. It “knows” from training data that:

• Asana is strong for team collaboration
• Monday.com is highly customizable
• ClickUp is feature-rich and affordable
• Notion is flexible but has a learning curve

Step 6: The model checks knowledge graphs for additional structured data about these products.

Step 7: The model analyzes sentiment. If recent sources are consistently positive about Asana for remote teams, that brand is weighted higher.

Step 8: The model applies safety and quality filters, ensuring no controversial or low-quality brands are recommended.

Step 9: The model generates a response that synthesizes all of this information, typically recommending 3-5 tools with specific reasoning for each.

Brands that appear in this response:

• Have strong parametric knowledge (training data presence)
• Rank well in search (RAG retrieval)
• Have authoritative citations (recognition and trust)
• Have positive consensus (sentiment analysis)
• Are current and relevant (recency)
• Have clear entity definitions (knowledge graphs)
• Pass quality and safety filters

Brands that don’t appear:

• Lack one or more of the above criteria

Practical Implications for GEO Strategy

Understanding these mechanisms points to clear strategic priorities:

Priority 1: Build Long-Term Training Data Presence

Invest in consistent, authoritative brand-building that will influence future model training:

• Earn regular press coverage in high-authority publications
• Publish thought leadership and original research
• Build Wikipedia and knowledge graph presence
• Create educational content that gets cited by others

Priority 2: Optimize for RAG Retrieval

Since many LLMs use real-time retrieval, maintain strong SEO fundamentals:

• Rank for key category and use-case queries
• Create clear, factual content that’s easy to extract and cite
• Build domain authority through quality backlinks
• Maintain fresh, updated content

Priority 3: Enhance Entity Understanding

Help AI systems understand your brand clearly:

• Implement comprehensive structured data (Schema.org)
• Maintain consistent brand information across platforms
• Build knowledge graph presence (Wikipedia, Wikidata)
• Create detailed product and company documentation

Priority 4: Manage Sentiment and Consensus

Actively shape how you’re discussed:

• Monitor brand mentions across the web
• Respond to negative coverage or misconceptions
• Earn positive citations from authoritative sources
• Build customer success stories and case studies

Priority 5: Monitor and Measure AI Visibility

You can’t optimize what you don’t measure:

• Systematically query major LLMs with relevant prompts
• Track brand mention rate and share of voice
• Analyze sentiment and positioning of mentions
• Benchmark against competitors

The Future: How LLM Decision-Making Is Evolving

LLM architectures and decision-making processes continue to evolve:

Agentic Search and Recommendations

Future AI systems will have “agents” that perform multi-step research, comparing sources, analyzing trade-offs, and even making purchases autonomously. These systems will likely use even more sophisticated decision trees and quality filters.

Multi-Modal Understanding

As LLMs incorporate images, video, and audio, decision-making will expand beyond text. Visual brand presence, video content quality, and audio mentions (podcasts, videos) will influence recommendations.

Real-Time Learning

Some systems may implement continuous learning, where user feedback and interaction data influence future recommendations dynamically. This creates opportunities for real-time optimization.

Personalization

LLMs will increasingly personalize recommendations based on user history, preferences, and context. This may fragment “universal” visibility into many personalized visibility profiles.

Conclusion

The “black box” of LLM decision-making isn’t as opaque as it first appears. While the exact probability calculations and weight distributions are complex, the underlying mechanisms are understandable and, importantly, influenceable.

To win in GEO, you need to influence multiple layers simultaneously:

• Build training data presence over time
• Optimize for real-time retrieval (RAG)
• Establish authority through citations
• Maintain positive sentiment and consensus
• Stay temporally relevant with fresh content
• Enhance entity recognition in knowledge graphs
• Pass quality and safety filters

The brands that succeed in AI search won’t be those that guess or hope for visibility. They’ll be those that systematically understand and optimize for each layer of the decision-making process.

The future of search is generative. The brands that understand how LLMs make decisions—and actively optimize for those mechanisms—will control access to customers in the age of AI.

Continue your GEO education:

• What is GEO? The Complete Guide - Foundational GEO concepts and strategies
• GEO vs SEO: Key Differences - Understand how GEO differs from traditional SEO
• Why SEO Isn’t Enough Anymore - The business case for investing in GEO