AEO Guide: Retrieval
How AI Systems Retrieve and Process Information
If competitors keep showing up in AI answers, but you don’t, that gap could come down to mechanics. Learn how AI systems retrieve and process information so you can optimize for citations and show up when customers start to consider their options.
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Gaining visibility in AI-powered search starts with understanding how these systems retrieve and process information. Traditional search and AI systems both use natural language processing (NLP) to analyze query intent, but they assess and deliver results in completely different ways.
Traditional search engines:
- Show a ranked list of relevant pages.
- Allow users to choose which sources to explore.
- Showcase multiple pages.
AI-powered answer engines:
- Retrieve relevant sources through query fan-out.
- Select sources and synthesize information for users.
- Provide source citations.
These differences are driven by a specific AI architecture called Retrieval-Augmented Generation (RAG).
What Is RAG?
Retrieval-Augmented Generation is the technical architecture that allows AI systems to access external information and integrate it into their responses, rather than relying solely on their training data.
RAG gives AI systems the ability to “look things up” to build a response.
RAG-enabled AI models can access up-to-date information from databases, websites, and documents to generate responses, instead of relying only on static training data.
RAG Components
Retrieval: The system searches for and extracts relevant external content.
Augmentation: The retrieved information enhances the AI’s knowledge for this specific query.
Generation: The system creates a response using both its training and the retrieved information.
RAG powers AI citations. When ChatGPT searches the web, when Perplexity shows sources, when Google AI Overview cites websites, that’s RAG in action.
What Is Retrieval?
AI systems retrieve outside sources to ground their responses. This prevents an over-reliance on stale training data or hallucinations and greatly increases the accuracy, relevance, and verifiability of the information they provide.
Retrieval is like the AI equivalent of a research assistant who, when asked a question:
- Quickly searches through a library,
- Finds relevant books and articles,
- Reads the pertinent sections,
- Evaluates their credibility, and
- Identifies which sources are most helpful for answering the question.
RAG-enabled systems do this in seconds, which is one reason RAG adoption is on the uptick.
Key Characteristics of AI Retrieval
Real-time: Happens in seconds when you submit a query.
Selective: AI chooses which sources to examine, not users.
Analytical: Content is processed and understood instead of matched.
Grounding-focused: Seeks factual information to anchor responses.
Quality-filtered: Evaluates source credibility and relevance.
What Is Retrievability?
Retrievability describes whether AI models can discover, understand, and cite your content during the retrieval process.
In AEO, retrievability plays the same role that crawlability and indexability play in traditional SEO. Just as content must be crawlable and indexable before it can rank, it must be retrievable before it can be cited.
Three things make content retrievable:
1. AI systems can find and access your content,
2. The meaning and context is clear at the passage level, and
3. Authority signals indicate your content deserves consideration.
Access and authority align neatly with SEO. However, traditional SEO optimizes entire pages. Because AI systems break content into smaller chunks and evaluate each one independently, AEO requires passage-level optimization.
To be retrievable, paragraphs need to be self-contained and meaningful on their own. Context and definitions can’t rely on information from other sections.
This approach has two advantages:
- A single page can be cited for multiple different queries, each pulled from different passages.
- One poorly optimized section won’t drag down other sections on the same page.
When AI Systems Retrieve vs. When They Don't
AI systems don’t retrieve external content for every query. They decide whether to use their trained knowledge or search for fresh information based on the query and how confident they are that they can produce an accurate answer with their data. This decision determines whether your content optimization efforts matter for a particular query.
Retrieval behavior varies across platforms and query types. What triggers retrieval on one platform may not on another. When planning AEO content updates, run your target queries on the platforms themselves to see exactly how they deliver information.
Query Types and Platform Behavior​
To help you get started, here are some standard query types and how different tools are currently handling them. Keep in mind that updates and personalization may also impact retrieval behavior.
Query Type |
Example |
Google AIOs |
AI Mode |
ChatGPT |
Perplexity |
Claude |
|---|---|---|---|---|---|---|
| Basic definitions | “What does SEO stand for?” | Retrieval | Retrieval | Training Data | Retrieval | Training Data |
| General knowledge | “What is photosynthesis?” | Retrieval | Retrieval | Training Data | Retrieval | Training Data |
| Historical facts | “When did WWII end?” | No AIO | Retrieval | Training Data | Retrieval | Training Data |
| Fundamental concepts | “How to calculate ROI?” | Retrieval | Retrieval | Training Data | Retrieval | Training Data |
How Do Different Platforms Handle Attribution?
While we can’t see how different AI platforms make retrieval decisions, we can observe differences in how they handle attribution.Â
Google AI Overviews
Google AI Overviews maintain strong source attribution by providing clear citations alongside synthesized answers, keeping original sources visible and preserving click-through potential.
Google AI Mode
Google AI Mode uses similar retrieval and citation approaches as AI Overviews, though our research shows it often pulls from different sources.Â
ChatGPT
When ChatGPT retrieves information from external sources, it adds citations after the response that link directly to the source websites.
Perplexity
Perplexity provides citations throughout its responses, linking directly to original sources. When drawing from multiple sources, it aggregates citations at the end of paragraphs.
Claude
When it uses its search functionality, Claude includes a drop-down list of sources at the top of the response, then appends citations after each relevant paragraph.
What Factors Influence AI Retrievability?
AI companies don’t publish their retrieval algorithms, so we depend on systematic testing and citation analysis to identify the content characteristics that consistently drive citations across platforms.
AI systems tend to favor sources that thoroughly address topics rather than providing surface-level coverage. This is in line with how the query fan out method seeks related information when generating a response.
Content that clearly explains concepts, defines terminology, and connects ideas seems to perform better in retrieval. AI systems need to not only understand what you’re saying, but also how each idea relates to the broader topic and user intent.
Authority indicators like backlinks and mentions in reputable publications appear to increase the likelihood of retrieval. AI systems seem to use these signals as proxies for trust when selecting sources to cite.
Content that demonstrates expertise across related topics within a subject area is retrieved more often because AI systems recognize topical authority through consistent coverage of related concepts.
Fresh, accurate content appears to receive priority, especially for time-sensitive topics. However, evergreen content with strong authority signals can maintain retrievability over time.
The Foundation for AEO Success
While other factors also influence AI search visibility, retrievability sets the baseline. AI systems can’t cite content they can’t access, parse, and evaluate at the passage level. Consistent citation comes from content that is structurally clear, self-contained, and supported by authority signals that hold across platforms. Making retrievability a core part of the content process helps sustain brand discoverability as AI search systems evolve.
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
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