How does LLM retrieval work for AI search? (AEO Guide)

Updated December 8, 2025

Introduction

Many B2B SaaS companies rank well on Google for their core category terms but remain completely invisible when prospects ask ChatGPT or Perplexity the same questions. Traditional SEO agencies report improved rankings and backlink growth, yet qualified leads decline because nearly 89% of B2B buyers now use generative AI as one of their top sources during the buying process. Your Google rankings become irrelevant when half your market researches using AI-powered search that operates on fundamentally different mechanics than traditional search engines.

Understanding how LLMs retrieve and synthesize information isn't just technical curiosity. It's the foundation for building a deliberate AI search strategy that captures the 89% of buyers who now start with AI. This guide explains the engineering behind LLM retrieval, shows you why traditional SEO tactics fail in this environment, and provides a framework for optimizing content that AI systems can find, trust, and cite. You'll learn about vector embeddings, query fan-out, reciprocal rank fusion, and how the CITABLE framework structures content for AI citation.

How LLMs actually find information (It's not Google indexing)

Google builds indexes of web pages and ranks them based on keywords, backlinks, and authority signals. When you search for "best CRM," it returns a ranked list of pages matching those terms. LLMs work completely differently.

They don't rank pages at all. Instead, they retrieve chunks of text based on semantic meaning, synthesize information from multiple sources, and generate new answers combining insights. The fundamental shift is from indexing pages to retrieving passages.

Understanding vector embeddings and semantic search

Vector embeddings are the foundation of how LLMs understand and retrieve information. Embeddings are high-dimensional vectors with hundreds to thousands of dimensions that capture semantic meaning of words, sentences, or entire documents.

Think of embeddings as coordinates in vast semantic space. Words with similar meanings occupy nearby positions:

• Similar concepts cluster together: According to research on word embeddings, adding vectors for "king" and "woman" while subtracting "man" equals the vector for "queen"
• Related terms maintain proximity: As Weaviate explains, "king" and "palace" appear nearby in vector space (both relate to royalty) but don't overlap (different concepts)
• Context determines meaning: Embeddings can distinguish whether "Python" refers to the snake or programming language based on surrounding text

Semantic search recognizes that "vehicle" and "car" are related or that different phrases convey the same meaning. Traditional keyword search sees only character strings, missing context entirely.

The practical impact: if you write "scheduling automation platform" but buyers search for "calendar management tool," keyword search might miss you. Vector embeddings understand semantic similarity and surface your content anyway.

The role of RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) optimizes a large language model's output by referencing authoritative knowledge bases outside its training data before generating responses. Platforms like Perplexity, ChatGPT with search, and Bing Chat all use RAG.

The process works in distinct phases:

1. User query: Convert question to vector embedding
2. Retrieval: Search for semantically similar content in databases or live web
3. Context assembly: Gather most relevant passages
4. Generation: Feed retrieved context to LLM as input
5. Response: Synthesize answer with source citations

As Microsoft's RAG architecture documents, an orchestrator determines which searches to perform, packages top results as context within prompts, sends that to the language model, and returns synthesized responses with citations.

RAG reduces AI hallucinations by grounding responses in verifiable data and allows LLMs to cite sources for user verification. Your content must be structured for passage retrieval, not page ranking, because LLMs extract specific paragraphs answering sub-components of questions rather than recommending full pages.

For detailed video walkthrough of how AI search optimization works in practice, Discovered Labs' founder Liam Dunne breaks down the complete process. Ethan Smith provides additional strategic context in his guide to AEO on Lenny's Podcast.

The technical workflow: Query fan-out and fusion

When prospects ask ChatGPT "What's the best CRM for startups?", the LLM doesn't search for that exact phrase. It generates multiple related queries, runs them simultaneously, and combines results through a process called query fan-out.

What is query fan-out?

Query fan-out is an information retrieval technique that expands single user queries into multiple sub-queries capturing different possible intents. Google popularized the term when introducing Google AI Mode.

In the Google I/O 2025 keynote, Head of Search Elizabeth Reid explained: "AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf."

Here's how it works in practice. When you ask "best protein for runners," the AI fans out into:

• Protein type variations: "best whey protein," "plant-based protein for athletes"
• Use case specifics: "post-run recovery supplements," "protein timing for endurance"
• Social proof searches: "protein powder reviews Reddit," "runner protein recommendations"
• Comparison queries: "whey vs plant protein for runners"

The scale varies by complexity:

• Simple queries: Most of the time, Gemini expands searches into about 5 related fan-outs, usually 2-4
• Standard questions: Google splits user queries into about 8 searches in documented examples
• Complex reasoning: Deep Search can issue dozens or hundreds of background queries and may take several minutes to complete

Important context: According to Nectiv's research analyzing 8,500+ prompts, ChatGPT performs searches in only 31% of prompts (not every time), averaging 2.17 searches per prompt when search is triggered.

Your content must answer implied questions, not just explicit ones. If someone asks "best project management software," address:

1. Use case variations: Remote teams, agencies, enterprise, startups
2. Feature-specific queries: Time tracking, budget management, resource allocation
3. Competitive context: Compared to Asana, vs Monday.com, Basecamp alternative
4. Integration needs: Slack integration, API access, Zapier compatibility

Our 11-step playbook for optimizing content for AI search explains how to map these query clusters systematically. This video from Surfer Academy demonstrates practical query fan-out analysis.

How reciprocal rank fusion determines the winner

After LLMs generate multiple sub-queries and retrieve results for each, they determine which sources to cite using reciprocal rank fusion (RRF).

RRF evaluates search scores from multiple previously ranked results to produce unified result sets. The process works in three steps:

1. Assign reciprocal rank scores: For each document in search results, calculate 1/(rank + k), where rank is position and k is a constant (typically around 60)
2. Aggregate across queries: Documents appearing in top positions across multiple search methods receive higher combined scores
3. Prioritize consistent performers: Sources ranking highly across different sub-queries win final citations

By prioritizing documents that consistently rank highly across different sources, RRF improves search relevance without traditional score normalization techniques.

This explains why breadth matters as much as depth. If your content ranks well for one sub-query but competitors appear across five variations, they win the citation. Your content needs topical authority across entire clusters of related questions, not just one perfect page.

Traditional SEO vs. LLM SEO: A comparison

The shift from keywords to concepts

The difference between optimizing for Google and optimizing for LLMs isn't just tactical - it's architectural. Traditional SEO focuses on entire pages as single units needing to show relevance as a whole. LLM optimization focuses on passage-level context where each content section should answer specific user questions or intents.

The citation landscape reflects this architectural difference. Comprehensive research from Higoodie analyzing B2B SaaS citations reveals that Reddit dominates with 6,326 citations and G2 with 6,097 citations across models' top-10 lists. Research from SearchAtlas confirms that traditional authority metrics like Domain Power and Domain Rating have weak or negative relationships with LLM visibility, with results showing "LLMs reward contextual relevance and diversity over authority."

When brand information is coherent across G2, websites, and third-party sources, AI models encounter fewer contradictions and cite you more frequently. Single mentions on your website carry less weight than consistent positioning across multiple verified platforms.

Watch this comprehensive case study from Liam Dunne showing how a B2B SaaS company ranked #1 in ChatGPT responses using these principles. Sam Dunning's complete guide to AI SEO provides additional strategic framework.

How to optimize for retrieval (The CITABLE framework)

Traditional content strategies fail in AI search because they optimize for wrong signals. You can't keyword-stuff your way into LLM citations. You need a framework designed specifically for how retrieval systems work.

Discovered Labs developed the CITABLE framework as a seven-part system ensuring content is optimal for LLM retrieval while maintaining excellent human readability. The methodology emerged from testing what content characteristics correlate with higher mention rates across ChatGPT, Claude, Perplexity, and Google AI Overviews.

Structuring entities for machine understanding

The first challenge is entity clarity. Named Entity Recognition (NER) identifies and categorizes named entities within text such as people, organizations, locations, and products. While LLMs can perform NER tasks, research shows they face notable performance gaps in traditional NLP tasks like Named Entity Recognition when compared to current state-of-the-art specialized technologies.

You must make entity recognition easy for AI systems. Here's the difference:

Clear entity statement:
"Calendly is a scheduling automation tool for sales teams."

This creates explicit entity-attribute pairs:

• Subject: Calendly
• Category: scheduling automation tool
• Audience: sales teams

Vague marketing language:
"We provide innovative scheduling solutions that help teams collaborate better."

This leaves LLMs guessing:

• Subject: ambiguous ("We")
• Category: generic ("solutions")
• Differentiator: meaningless ("innovative")

The CITABLE framework's "C" (Clear entity & structure) and "E" (Entity graph & schema) components address this directly:

• Lead with 2-3 sentence BLUF: What it is, who it's for, when to use it
• Use subject-verb-object structure: "Discovered Labs is an AEO agency that helps B2B SaaS companies get cited by ChatGPT"
• Define category explicitly: State your product category in first paragraph
• Include schema markup: Organization and SoftwareApplication schema types help LLMs understand entity relationships

According to research from Microsoft, contextual understanding allows LLMs to analyze surrounding text to determine meaning, improving recognition accuracy. Clear statements reduce ambiguity and increase the probability your brand gets correctly identified and cited.

Use Discovered Labs' AEO Content Evaluator tool to test whether existing content has clear entity definition or needs restructuring. Our diagnostic checklist for low AI presence walks through entity optimization systematically.

Why third-party validation signals matter (Reddit & Reviews)

AI systems prioritize sources they can cross-validate across multiple platforms. G2 provides verified reviews, detailed feature descriptions, competitive comparisons, and data-rich category positioning. When brand information is coherent across G2, websites, and third-party sources, AI models cite you more frequently.

Grounding is fundamental to RAG systems. Grounding connects model outputs to real-world facts or trusted data sources, ensuring responses base themselves on verifiable information rather than learned patterns alone.

AI retrieval systems weight certain source types more heavily:

• Community-curated platforms: Reddit accounts for 46.7% of Perplexity citations, 21% of Google AI Overviews, and 11.3% of ChatGPT citations
• Review aggregators: G2, Capterra, and similar platforms provide user consensus signals
• Expert forums: Stack Overflow, specialized subreddits, industry communities
• News and publications: Established media outlets with editorial standards

Reddit has special data-sharing agreements with AI companies, including a $60 million annual licensing deal with Google and partnerships with OpenAI, strengthening its citation power. This explains Reddit's dominance with over 6,300 citations in B2B SaaS categories.

The CITABLE framework's "T" (Third-party validation) focuses explicitly on building these signals:

1. Maintain consistent NAP data: Name, address, phone across all platforms
2. Build G2 presence: Encourage customers to leave detailed, verified reviews
3. Cultivate Reddit mentions: Participate authentically in relevant subreddits
4. Secure editorial coverage: Earn mentions in industry publications
5. Fix conflicting information: AI models skip citing brands with contradictory data across sources

Our Reddit marketing service helps B2B companies systematically build credible mentions in relevant subreddits. Learn more about our approach to third-party validation in our complete Answer Engine Optimization Playbook.

For masterclass on why third-party validation matters, watch Ethan Smith's detailed breakdown referenced earlier and Noah St. John's tips for dominating AI search.

Measuring the impact of AI visibility

You can't optimize what you don't measure. Traditional SEO metrics like keyword rankings and domain authority don't translate to AI search performance. You need metrics reflecting citation behavior.

Key measurement frameworks include:

Citation Rate: The percentage of times your brand appears when AI platforms answer buyer-intent queries in your category. This measures how often your brand appears within AI-generated answers for target prompts. Citation frequency is the primary metric measuring how often your brand or content is referenced across major AI platforms.

Share of Voice (SoV): The number of citations your site gets divided by total available citations. When ChatGPT, Perplexity, or Claude recommend software, they cite specific platforms first. Your share of voice shows whether you're in that shortlist. Citation share of voice (C-SOV) is the #1 metric for AI visibility.

Source Diversity Score: Measures the breadth of authoritative surfaces where your brand appears. AI models trust brands with "wide footprint" across forums, review platforms, expert blogs, documentation, Reddit threads, niche communities, and third-party editorial content.

AI-Referred Conversions: Track with UTM parameters (utm_source=chatgpt, utm_source=perplexity) and add "How did you hear about us?" surveys in onboarding flows. Research from Semrush shows that the average AI search visitor is 4.4 times as valuable as the average visit from traditional organic search, based on conversion rate.

Why does AI-referred traffic convert better? As Ahrefs documented, by the time AI search users visit your site, they've likely already compared options and learned about your value proposition, making them much more likely to convert. Ahrefs found AI search visitors convert at 23 times the rate of traditional organic search visitors for their platform, with 12.1% of signups coming from 0.5% of traffic.

Tools for tracking AI visibility:

• Semrush AI Visibility Toolkit - Tutorial from Backlinko
• ReachLLM demo - Audit your brand in minutes
• Discovered Labs proprietary auditing technology - Tests thousands of buyer-intent queries

Calculate potential pipeline impact using our ROI calculator to model improvements in citation rates.

Case study: 4x growth in AI-referred trials in 4 weeks

One B2B SaaS company came to us ranking well in Google but completely invisible in ChatGPT and Perplexity for their core category. Prospects were asking AI for recommendations and receiving shortlists that never included them.

Before state - competitive gap:

• Competitor A: 38% citation rate across core queries
• Competitor B: 29% citation rate
• Competitor C: 24% citation rate
• Client: 5% citation rate

We implemented the CITABLE framework across their content, restructured existing pages for passage retrieval, published new answer-focused articles targeting query fan-out variations, and built systematic third-party validation through Reddit and G2.

After implementation:

AI-referred trials grew from 550 per month to 2,300+ in four weeks, representing a 4x increase. The company's citation rate reached 42%, overtaking all three primary competitors and becoming the most-cited brand in their category. Cost efficiency improved significantly, with AI-sourced leads requiring lower acquisition costs than traditional paid channels.

The company's VP of Marketing noted that AI-referred leads arrived already educated on product capabilities and competitive positioning, shortening sales cycles and improving qualification rates compared to cold inbound from traditional search.

Read the complete methodology and timeline in our detailed case study.

Frequently asked questions about AI search

What does AEO stand for?
AEO stands for Answer Engine Optimization. It's the practice of structuring content so AI platforms like ChatGPT, Claude, and Perplexity cite your brand in their answers.

What is GEO in AI?
GEO stands for Generative Engine Optimization, distinct from geography. It focuses specifically on getting cited by LLMs like ChatGPT, while AEO includes broader AI-powered search features like Google's AI Overviews.

What is LLM SEO?
LLM SEO refers to optimizing content for Large Language Model retrieval systems. It emphasizes semantic relevance, passage-level optimization, and entity clarity rather than traditional keyword density and backlinks.

How long does it take to see results from AEO?
According to HyperMind's research, most brands see initial citations within 2-3 months of implementing focused AEO strategy. Results depend on competition level, content quality, and consistency of implementation.

Do I need to stop doing SEO if I focus on AEO?
No. As we explain in our comparison guide, traditional SEO remains valuable for Google rankings while AEO ensures you're also visible in ChatGPT, Claude, and Perplexity where buyers increasingly research.

Key terminology glossary

Vector Embedding: High-dimensional numerical representation of text (hundreds to thousands of dimensions) capturing semantic meaning, allowing LLMs to find conceptually similar content even with different wording.

RAG (Retrieval-Augmented Generation): Process where LLMs retrieve relevant external information before generating responses, reducing hallucinations and allowing citation of sources.

Grounding: Connecting LLM outputs to verifiable real-world data sources, essential for factual accuracy and citation trustworthiness.

Query Fan-Out: Technique where AI systems expand single user queries into multiple related sub-queries to capture different aspects of user intent.

Reciprocal Rank Fusion (RRF): Algorithm that combines rankings from multiple search methods to determine which sources get cited in final AI-generated answers.

Zero-Click Search: When users get complete answers from AI platforms without clicking through to source websites, representing both challenge and opportunity for content creators.

Conclusion

LLMs have fundamentally changed how search works, so your strategy must change too. They don't rank pages based on keyword density and backlinks. They retrieve semantically similar passages, validate information across multiple sources, and synthesize answers citing the most trusted content.

Your Google rankings won't save you if ChatGPT recommends three competitors and never mentions your name. The good news: AI search visibility is engineerable, not mysterious. Clear entity definition, passage-optimized structure, and systematic third-party validation through platforms like Reddit and G2 give LLMs the signals they need to cite you.

Stop guessing how the black box works. Request an AI Visibility Audit from Discovered Labs to see exactly how ChatGPT, Claude, and Perplexity view your brand today. We'll show you where you appear (or don't) across buyer-intent queries, which competitors dominate your category, and specific content gaps holding you back.

Our month-to-month AEO service implements the complete CITABLE framework for you, with weekly citation tracking showing exactly what's working. No 12-month contracts required. Just measurable improvements in how often AI platforms recommend your brand when prospects ask for solutions. Explore our comprehensive SEO services or learn why traditional SEO agencies struggle with AI search.