How LLMs Decide What to Recommend: Inside AI Decision-Making

When you ask ChatGPT "What's the best project management tool?", it doesn't search a database or run an algorithm. Instead, it generates a response based on patterns learned during training. Understanding how this works is crucial for anyone trying to influence AI recommendations.

This article explains the technical foundations of how large language models make recommendations.

The Basics: How LLMs Generate Text

Large language models like GPT-4, Claude, and Gemini are trained to predict the next word in a sequence. When you ask a question, the model generates a response word by word, each choice influenced by:

1. Training data: Text the model learned from
2. Context: Your question and conversation history
3. Model architecture: How the neural network processes information
4. Sampling parameters: Temperature and other generation settings

A Simplified Example

When asked "What's a good CRM for small businesses?", the model might process:

Input: "What's a good CRM for small businesses?"

Model considers:
- What CRMs appeared frequently in training data
- What CRMs were mentioned positively
- What CRMs were associated with "small business"
- What patterns exist in similar recommendation contexts

Output: "For small businesses, I'd recommend considering Salesforce,
HubSpot, or Zoho CRM..."

The model isn't "choosing" in a conscious way—it's generating statistically likely continuations based on learned patterns.

What Influences LLM Recommendations?

1. Training Data Frequency

The more often a brand appears in training data, the more likely it is to be mentioned. This is why established brands with extensive web presence tend to be recommended more often.

Factors that increase training data presence:

• Large website with many indexed pages
• Frequent mentions in news and publications
• Active discussions on forums and social media
• Wikipedia articles and educational content
• Review sites and comparison articles

2. Association Strength

LLMs learn associations between concepts. A brand strongly associated with a category is more likely to be recommended for that category.

Example associations:

• "CRM" → Salesforce, HubSpot, Zoho
• "Project management" → Asana, Monday, Trello
• "Email marketing" → Mailchimp, ConvertKit, Klaviyo

These associations form through repeated co-occurrence in training data.

3. Sentiment and Context

LLMs learn not just what brands exist, but how they're discussed:

• Positive sentiment: "HubSpot is excellent for startups"
• Negative sentiment: "Company X has terrible customer service"
• Neutral mention: "Company Y offers CRM software"

Positive sentiment associations increase recommendation likelihood.

4. Specificity Matching

When users ask specific questions, LLMs try to match specificity:

• "Best CRM" → General recommendations (Salesforce, HubSpot)
• "Best CRM for real estate agents" → More specific matches
• "Best CRM under $50/month for 5 users" → Very specific criteria

Brands with content addressing specific use cases get recommended for specific queries.

5. Recency (for models with web access)

Models like GPT-4 with browsing and Perplexity can access current information. For these, recency matters:

• Recent news coverage
• Updated website content
• Current reviews and discussions

The Role of "Consensus"

LLMs exhibit a form of "consensus bias"—they're more likely to recommend things that multiple sources agree on.

How Consensus Forms

If your brand is mentioned positively by:

• Industry publications (TechCrunch, Forbes)
• Review sites (G2, Capterra)
• Educational resources
• Discussion forums
• Competitor comparisons

The model learns that "multiple sources recommend Brand X for Category Y" and becomes more likely to generate that recommendation.

Implications for Brands

To build consensus:

1. Get covered by multiple authoritative sources
2. Earn positive reviews on multiple platforms
3. Be included in comparison articles
4. Have consistent messaging across sources

Understanding Model "Confidence"

LLMs have varying levels of "confidence" in their recommendations, reflected in language:

High confidence (strong training signal):

"Salesforce is the industry-leading CRM platform"

Moderate confidence:

"Many businesses find HubSpot to be a good option"

Low confidence (weak training signal):

"You might also consider lesser-known options like..."

Brands with stronger training data presence get more confident recommendations.

How RAG Changes the Equation

Retrieval-Augmented Generation (RAG) is changing how LLMs make recommendations. Instead of relying solely on training data, RAG systems:

1. Retrieve relevant documents from a knowledge base
2. Use retrieved information to generate responses

RAG Implications

With RAG systems (like Perplexity):

• Current information matters more
• Your website content directly influences responses
• SEO-style optimization becomes relevant
• Fresh content can override training data

Optimizing for RAG

• Keep website content current
• Structure content for easy extraction
• Use clear, quotable statements
• Maintain strong domain authority (affects retrieval ranking)

The Temperature Factor

LLM outputs are influenced by "temperature"—a parameter controlling randomness:

• Low temperature (0.0-0.3): More deterministic, consistent responses
• High temperature (0.7-1.0): More varied, creative responses

What This Means for Recommendations

At low temperature, the model gives more predictable recommendations (usually the top brands by training data presence).

At high temperature, lesser-known brands have a higher chance of being mentioned as the model explores more varied outputs.

Most production AI assistants use moderate temperature, balancing consistency with variety.

Limitations of LLM Recommendations

Understanding limitations helps set realistic expectations:

Training Data Cutoffs

Models without web access have knowledge cutoffs:

• GPT-4: Training data up to April 2024
• Claude: Training data up to early 2024

New products launched after cutoffs won't appear in recommendations unless the model has web access.

Hallucination Risk

LLMs can generate plausible-sounding but incorrect information:

• Inventing features that don't exist
• Stating incorrect pricing
• Confusing similar brands

This is why factual accuracy in your content matters—you want the model to have correct information to draw from.

Inconsistency

The same question asked multiple times may yield different recommendations due to:

• Sampling randomness
• Context variations
• Model updates

Practical Implications for Brands

Based on how LLMs work, here's what actually influences recommendations:

High Impact

1. Extensive, high-quality web content that increases training data presence
2. Coverage in authoritative sources (news, publications, Wikipedia)
3. Positive sentiment across multiple platforms
4. Strong category associations through consistent messaging
5. Structured data that helps models understand your brand

Moderate Impact

1. Review site presence (G2, Capterra, Trustpilot)
2. Social media mentions (large-scale)
3. Forum discussions (Reddit, Quora, Stack Overflow)
4. Comparison articles that include your brand

Lower Impact

1. Paid advertising (doesn't directly affect training data)
2. Social media follower counts (not directly learned)
3. Website design (models don't "see" visual design)

Future Developments

LLM recommendation systems are evolving:

Real-Time Information

More models are gaining web access, making current information more important than historical training data.

Personalization

Future models may personalize recommendations based on user context, preferences, and history.

Sponsored Integration

Official ad placements (ChatGPT Ads, Perplexity Sponsored) provide a direct path to recommendations outside organic influence.

Multimodal Understanding

Models increasingly understand images, video, and audio, expanding how brand information is processed.

Key Takeaways

1. LLMs recommend based on learned patterns, not search algorithms
2. Training data frequency and sentiment are primary factors
3. Consensus across multiple sources increases recommendation likelihood
4. RAG systems make current content more important
5. Building organic AI visibility takes time but creates lasting presence

---

Understanding how LLMs work is the first step to influencing their recommendations. Contact AdsX to learn how we can help improve your brand's AI visibility based on these technical foundations.