AEO Guide: Inside the AI Retrieval Process
How Systems Decide What To Cite
Understanding how AI systems retrieve, evaluate, and generate answers can help you craft a more strategic approach to answer engine optimization.
In This Article
Additional Resources
To get AI systems to cite your content, you need to know how they decide what’s worth sharing. Each phase of the retrieval process presents specific optimization opportunities. By understanding where your content succeeds or fails in this pipeline, you can make targeted improvements rather than guessing what might work.
While some retrieval phases happen entirely within the large language model (LLM), others are directly influenced by how you structure and present your content.
When your content isn’t getting cited but your competitor’s is, the problem could be in:
- Query analysis: AI systems don’t recognize your content as relevant to the user’s intent.
- Retrieval: AI systems don’t find your content while seeking sources.
- Content analysis: AI systems can’t extract or understand your content properly.
- Quality evaluation: AI systems don’t trust your content or don’t see your site as authoritative.
- Response generation: Competitors provide stronger quality signals or better-formatted content.
Understanding these phases can help you prioritize content updates that improve citation potential. For example, if you already cover a topic but fail to answer related questions users ask, you can expand supporting content into clusters and strengthen internal links. That structure signals relevance more clearly and increases the likelihood that systems surface your content during retrieval.
The following framework describes how AI systems generally retrieve and cite content. While we use Google AI Overviews and ChatGPT as examples, each platform relies on its own retrieval architecture. The phases below illustrate common retrieval patterns, not exact platform-specific processes.
Phase 1: Query Analysis
First, the AI system breaks down the user’s question to understand what they’re asking. It identifies the intent (are they looking for information, comparing options, or ready to take action?). Then it decides if it can accurately answer the query with existing information or if it needs to retrieve fresh content.
Google AI Overviews: Definition Query
When you search “what is index bloat” in Google, Gemini, the LLM behind AIOs and AI Mode, analyzes:
- Intent: Informational (seeking definition and explanation)
- Key concepts: Index bloat, search indexing, crawling, technical SEO
- What it needs to generate a response: Authoritative sources that explain the concept clearly, including what it is and why it matters
ChatGPT: Product Query
When you prompt ChatGPT with “best running shoes for marathon training”, the LLM breaks this down into:
- Intent: Commercial investigation (researching before purchase)
- Key concepts: Running shoes, marathon training, selecting shoes, product recommendations
- What it needs to generate a response: Fresh content with product comparisons and reviews
Phase 2: Source Retrieval
When models need additional content to formulate a response, RAG-enabled LLMs like Gemini, Claude, Perplexity, and ChatGPT use query fan-out to find information. The system breaks a single question into multiple related sub-queries that reflect different angles of the original intent.
AI models run these searches simultaneously across multiple connected sources like the web, knowledge bases, or document stores. They rely on semantic matching to identify pages based on meaning, rather than exact keyword matches. .
This step casts a wide net. The system returns a broad set of potentially relevant documents or passages, which it will evaluate, filter, and refine.
Google AI Overviews: Retrieving Index Bloat Content
For “what is index bloat”, Gemini searches the Google web index and may identify candidate pages from:
- Established SEO publications
- Technical SEO agency blogs
- Google Search Central documentation
- SEO education platforms
It prioritizes pages from domains with demonstrated expertise in technical SEO and search engine mechanics.
ChatGPT: Retrieving Running Shoe Content
For “best running shoes for marathon training”, ChatGPT searches its connected web sources and identifies candidate pages from:
- Running specialty publications
- Athletic gear review sites
- Marathon training resources
- Product review aggregators
It prioritizes recently published content and pages from domains with strong topical authority in running and athletic equipment.
Phase 3: Extraction and Content Analysis
The system extracts text from the retrieved pages and strips away anything that distracts from meaning. It focuses on content it can reliably parse and evaluate.
It processes:
- Main body text (headers, paragraphs)
- Meta title and meta description
- Alt text for images
- Structured data
And ignores:
- Ads, popups, and navigation menus
- Repetitive elements like headers, footers, and cookie notices
- Styling and layout code like CSS and JavaScript
The model then analyzes the extracted text to assess meaning and determine how relevant it is to the query. It identifies semantically related terms and entities like people, places, products, and concepts and evaluates how well the content aligns with the original query.
Google AI Overviews: Analyzing Index Bloat Content
For “what is index bloat”, Gemini retrieves pages from topically relevant sites like SEO blogs and publications. After stripping away navigation clutter, it analyzes passages that explain how search engines get bogged down indexing duplicate content, thin pages, and auto-generated URLs.
The system doesn’t need to see the exact phrase “what is index bloat.” it recognizes definitional content by looking for clear explanations, concrete causes, and performance impacts.
ChatGPT: Analyzing Running Shoe Content
For “best running shoes for marathon training”, ChatGPT may retrieve pages from running blogs, gear review sites, and training resources. It extracts product specs alongside expert testing insights. When it encounters analysis about carbon-fiber plates reducing fatigue during 20+ mile runs, it connects those biomechanical benefits directly to marathon performance needs. The system understands that content about “long-distance training” addresses marathon queries, even without an exact phrase match .
Phase 4: Quality Evaluation and Source Selection
Before selecting final sources, the system evaluates the quality of the passages and the site’s authority.Â
To assess quality, it checks if the content directly answers the query. The system looks for thoroughness, accuracy, and supporting evidence like examples or data.
To assess authority, it evaluates the author’s expertise and credentials, the domain’s reputation, and external signals like shares or backlinks. This is similar to Experience, Expertise, Authoritativeness, and Trust (EEAT) in SEO.
Google AI Overviews: Evaluating Index Bloat Sources
For “what is index bloat”, Gemini prioritizes pages from established SEO publications with technical depth, real audit examples, and specific metrics (crawl stats, index coverage reports). It looks for author credentials in technical SEO and citations from other authoritative SEO resources.
It deprioritizes general marketing blogs without technical examples or outdated content that doesn’t reflect current Google crawling behavior.
ChatGPT: Evaluating Running Shoe Sources
For “best running shoes for marathon training”, ChatGPT may prioritize pages from running specialty publications with detailed testing methodologies and specific performance metrics. It looks for author credentials like competitive running experience or coaching certifications.
It deprioritizes generic product listings without testing data or pages from general fitness blogs without running-specific expertise.
Phase 5: Response Generation and Citation
In the final phase, the LLM selects which sources to use and generates a response. Unlike traditional search results that send you to individual pages, these AI responses combine insights from multiple sources into a unified answer.
Google AI Overviews for Index Bloat
For the query “what is index bloat”, Google displays the following:
It provides a definition and links to sources at the end. Then it covers the causes and potential negative impacts of index bloat. These additional sections come from a query fan-out where the system explores related subtopics and may cite various URLs.
A single page or site can appear multiple times when its content clearly addresses several parts of the expanded query set.
ChatGPT Running Shoe Recommendations
For the query “best running shoes for marathon training”, ChatGPT returns:
Along with specific recommendations based on expert testing, ChatGPT provides information about selecting the right shoe for you.
What Does This Mean for AEO?
When you understand how LLMs interpret queries and evaluate sources, you can prioritize updates with the greatest upside. You’ll see which pages need retrievability fixes, where new content can close query coverage gaps, how query fan-out should shape your internal linking strategy, and where to reinforce trust signals. That focus supports a stronger AEO strategy and leads to more consistent citation opportunities.
Head of SEO
Mark Wesley designs SEO and AEO strategies that improve discoverability across traditional search results and answer-based environments, helping brands stay visible as search experiences and answer formats continue to evolve.
Updated Jan 12, 2026
Continue Your AEO Journey
Show up everywhere
your
audience searches.
Increase brand awareness and engagement with a custom SEO and AEO strategy that puts you in front of prospects wherever they seek information.