We asked the same AI Search question to ChatGPT, Gemini, Perplexity and three Ollama models. Here's how each AI answered and what we learned from the comparison.

Over the past few weeks we've been spending a lot of time studying how AI search engines retrieve and cite information. During one of our latest experiments we came across a simple question that generated surprisingly different answers depending on which AI model we asked.

Instead of changing the wording or trying to optimize the prompt, we did the opposite.

We gave exactly the same prompt to multiple AI models without any follow-up questions, additional context or prompt engineering.

Our goal wasn't to declare a winner. We simply wanted to understand how different language models reason about the same problem.

Here is the prompt we used.

You are given three articles about the same topic.

One is a product page.

One is Wikipedia.

One is the most comprehensive article.

Without using search rankings as an argument, explain why an AI search engine might cite the product page first.

The question itself was inspired by a real case study we recently published after noticing that Google AI Overview cited a product page before Wikipedia and before a much more detailed article.

What happened next was fascinating.

Although every AI model reached a broadly similar conclusion, each one emphasized completely different aspects of the problem.

ChatGPT

ChatGPT focused on concepts such as information density, entity clarity, structured content and what it described as a lower "reconstruction cost" for language models. Rather than assuming Google simply prefers one type of page over another, it argued that concise pages with explicit facts may require less inference when generating an answer.

Our thoughts

This was one of the most balanced responses.

It clearly separated plausible explanations from certainty and introduced several concepts that align closely with the observations we've been making while studying AI Search.

Score: 9.8/10

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Gemini

Gemini approached the question from a more technical perspective.

Its answer discussed Retrieval-Augmented Generation (RAG), semantic embeddings, structured data, chunk extraction and entity-attribute mapping. It framed the problem almost entirely through the lens of modern retrieval architectures.

Our thoughts

Technically impressive and very detailed.

However, parts of the explanation felt more like engineering theory than conclusions that can currently be verified through public documentation.

Score: 9.4/10

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An AI search engine selects its citations based on how well source content fits its mathematical retrieval and synthesis pipeline—specifically through Retrieval-Augmented Generation (RAG)—rather than traditional domain prestige or article length.

Here is why an AI search engine might cite the product page over Wikipedia or a more comprehensive, long-form article:

1. Intent Matching and Actionability

• Commercial and Transactional Nuance: If the underlying user prompt carries commercial or transactional intent (e.g., specific dimensions, pricing, technical specs, compatibility, or availability), Wikipedia's high-level overview or an editorial guide's generic advice won't answer the question directly.

• Primary Source Authority: For specific product attributes, official product pages act as the primary, authoritative entity. The AI prioritizes citing the source of truth over third-party summaries.

2. High Information Density and Direct Extractability

• Concise Entity-Attribute Value Mapping: Large Language Models (LLMs) evaluate text chunks for information density. A product page typically presents attributes clearly (e.g., "Weight: 1.2 kg", "Battery Life: 18 hours").

• Chunk Efficiency: A comprehensive, 3,000-word article often buries key facts in prose or narrative context. When an AI extracts a 150-word chunk to populate its context window, a structured product page chunk offers a higher density of verifiable facts with less fluff.

3. Structured Machine-Readable Data (Schema Markup)

• Explicit Semantic Parsing: Product pages frequently leverage rich Schema.org metadata (Product, Offer, AggregateRating, FAQPage).

• Reduced Hallucination Risk: Structured data gives the AI engine machine-readable confidence. AI models prefer sources where claims can be validated without complex, ambiguous natural-language processing.

4. Vector Embedding Proximity (Semantic Precision)

• Direct Alignment: AI search translates the user’s query into a multi-dimensional vector embedding. A product page that addresses a specific solution or use-case maps more closely in vector space to a targeted prompt than a general encyclopedic page (Wikipedia) or a broad pillar article.

5. Content Freshness and Temporal Accuracy

• Real-Time Data: Product pages update frequently regarding specs, stock, and current versions. Wikipedia and long-form blogs often suffer from content decay. For topics where recency matters, RAG models favor the most recently updated content chunk.

Summary Comparison

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Perplexity

Perplexity took a very practical approach.

Instead of focusing on Google's internal systems, it explained that citation order reflects which source best supports a specific statement rather than which article is objectively the best. It also highlighted an important distinction between comprehensive content and citation usefulness.

Our thoughts

Probably the most pragmatic answer.

Its conclusion—that important facts should be explicit, current and easy to extract—fits remarkably well with the direction AI Search appears to be moving.

Score: 9.8/10

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An AI search engine might cite the product page first because citation order usually reflects usefulness for the specific answer, not which article is longest or most authoritative in the abstract. If the user asks about a product’s price, features, availability, compatibility, or intended use, the product page is likely to contain the clearest first-party evidence.

Why the product page wins

A product page often presents information in a format that is easy for an AI system to extract and verify: product name, specifications, variants, pricing, reviews, availability, and structured data. These details answer narrow parts of a user’s question directly, without requiring the system to interpret a long explanation. Product schema is specifically designed to describe attributes such as offers, ratings, and availability, which makes the page more machine-readable.foglift

The page may also be the most relevant source for facts that only the manufacturer or seller can confirm. Wikipedia can explain what the product is or place it in a broader category, but it may not contain current commercial details. The comprehensive article could provide better analysis, yet its information may be spread across several sections, mixed with comparisons, or written for a broader audience.

There is also a difference between comprehensiveness and citation usefulness. An AI answer does not necessarily need the article that covers every angle. It needs a source that cleanly supports the sentence it is generating. Google describes AI search as retrieving pages and then reviewing specific information from them to build an answer, with links supporting those claims.developers.google

That means the product page may be cited first for a factual statement such as “the product includes feature X,” while Wikipedia or the longer article may appear later for history, context, or independent evaluation. The ordering is therefore less a verdict on editorial quality than a reflection of which source offers the strongest, most direct evidence for the immediate claim.

The practical lesson for website owners is simple: make important facts explicit, current, independently verifiable, and easy to extract—not merely buried inside well-written prose.

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Ollama – Qwen 3 8B

Qwen emphasized structured content, user intent, multimedia elements and freshness.

Its explanation covered several reasonable possibilities and showed a good understanding of how commercial pages are often designed.

Our thoughts

A solid response overall, although some conclusions relied more on general assumptions than on observations specific to the prompt.

Score: 8.3/10

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An AI search engine might list the product page first for various reasons related to content structure, user intent, and the efficiency of the information extraction process. Here are some key reasons, not referring to search ranking:

1. Structure and parsing ease
Product pages are often designed with a clear organization (e.g., "Features," "Price," "Reviews," "Videos"), which facilitates the rapid extraction of specific information. AIs can use structured data schemas (such as schema.org) to automatically identify details such as price, availability, user ratings, or technical specifications. This makes product pages more "readable" for an algorithm, even if they aren't always the most detailed.

2. User intent
If the user is searching for information with the goal of purchasing a product, the product page is the most direct answer. AIs can recognize purchasing intent (e.g., keywords like "buy," "price," "reviews") and prioritize content that immediately satisfies that query, even if they aren't the most comprehensive sources. The product page is a decision point for the user, not just a data repository.

3. Interactive and multimedia content
Product pages often include images, videos, or interactive elements (e.g., simulators, demos) that an AI might deem more relevant for a complete user experience. Even if the content is less detailed, the presence of visual elements could be seen as an advantage for quickly understanding a product.

4. Frequent updating and freshness
Product pages are often updated regularly to reflect new offers, models, or promotions. AIs might favor more recent content, even if it isn't the most comprehensive, because it's deemed more useful for immediate purchasing decisions. The product page might therefore appear "more current" than an encyclopedia article or technical document.

5. Context recognition
An AI could automatically associate the request with a specific context (e.g., "buy a product," "compare prices," "evaluate an offer"). In this case, the product page is the most relevant content in that context, even if it isn't the most detailed or comprehensive.

In short, AI might cite the product page first because it directly addresses the user's intent, is accessible, and offers practical information (price, reviews, availability) that meets a user's immediate needs, even if it isn't the most comprehensive source.

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Ollama – Gemma 3

Gemma focused heavily on user intent.

According to its explanation, product pages are often the best match because they directly satisfy what many users are trying to accomplish when searching for a product.

Our thoughts

Easy to read and logically structured.

While accurate, it remained fairly general and didn't explore concepts such as entity clarity or information density in much detail.

Score: 8.0/10

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Okay, here’s an explanation of why an AI search engine might prioritize citing the product page first, even if it's not the most comprehensively researched source, without relying on search ranking algorithms:

The Core Reasoning Lies in Intent and Relevance to the Query:

An AI search engine isn’t simply looking for the “best” or “most complete” answer. It's designed to fulfill a user intent. Here's what likely drives prioritizing the product page:

1. Directly Addressing the User's Need: When someone types in a query related to a product (e.g., "noise-canceling headphones"), the initial impulse is often to find that product and learn about it. The product page, by definition, directly addresses this immediate need. It provides information relevant to purchasing or learning about a specific item - its features, price, availability, reviews etc.

2. Specificity & Context: Product pages provide a tightly focused context. They're built around a single entity – the product itself. The Wikipedia entry and the comprehensive article might cover broader aspects of a category (e.g., "headphones" or “noise-canceling technology”), which can be overwhelming for a user who just wants to understand one particular item.

3. Structured Data & Immediate Answers: Product pages are often designed with easily digestible information – bullet points, specifications tables, images, etc. An AI could interpret this structure as indicating the most relevant source for a quick answer to a typical question: "What are the features of these headphones?" The product page is optimized to deliver that directly.

4. Signal of User Action: An AI might infer that if users frequently click on product pages when searching for specific items, it’s a strong signal that those pages offer what they're actually looking for – the solution or information they need.

In short, an AI is likely prioritizing the product page because it represents the most direct and immediate fulfillment of the user's presumed intention: to find information about or purchase a specific product.

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Ollama – Llama 3.1

Llama highlighted structured content, freshness and direct answerability.

Its reasoning followed a traditional information retrieval perspective and explained why product pages often provide concise, machine-friendly answers.

Our thoughts

A clear and useful response.

However, much of the reasoning was broad enough to apply to almost any search engine rather than this specific AI Search scenario.

Score: 7.8/10

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An AI search engine might cite the product page first due to its relevance and purpose-driven content structure. Here are some possible reasons:

1. Direct answerability: A product page is designed to provide clear answers about a specific product or service, making it a direct source of information on the topic. The AI search engine may prioritize this source because it can confidently extract and return accurate answers to user queries.
2. Structured content: Product pages often follow a standardized structure, which makes it easier for an AI to understand and parse the content. This structured format allows the AI to identify key pieces of information, such as product features, specifications, and prices, making it a reliable source for extracting specific data points.
3. Freshness and timeliness: Product pages are typically updated frequently to reflect changes in the market, pricing, or product offerings. An AI search engine may prioritize this source because it provides up-to-date information that is more relevant to current user queries.
4. User intent alignment: A product page is often created with a clear understanding of the user's intent: to learn about and potentially purchase the product. The AI search engine may recognize this alignment between the user's query and the purpose of the product page, making it a more suitable source for answering the user's question.
5. Scalability: Product pages are often generated through an automated process, which makes them easily scalable and accessible in large quantities. An AI search engine may be able to efficiently crawl and index these sources, allowing it to return answers from this domain with high confidence.

In contrast, a comprehensive article or Wikipedia page might provide more context and depth but may not offer the same level of direct answerability or structured content that a product page does. The AI search engine may prioritize the product page as a primary source because it can rely on its accuracy, relevance, and timeliness to return answers to user queries.

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What We Learned

Perhaps the most interesting part wasn't which model scored highest.

It was how much agreement existed between completely different AI systems.

Without communicating with each other, most of them independently highlighted the same recurring themes:

• Information density.
• Structured content.
• Clear entity relationships.
• Direct answerability.
• User intent.
• Easy information extraction.

None of these observations prove that Google AI Overview uses these exact signals when selecting citations. Google has never published a complete description of its AI citation process, and many different ranking and retrieval systems are likely involved.

However, the consistency across multiple independent AI models is interesting. It suggests that these characteristics are widely considered valuable for retrieval-based AI systems, regardless of the specific implementation.

Our Conclusion

One prompt is obviously not enough to judge an AI model.

Different prompts produce different strengths, different weaknesses and sometimes completely different reasoning.

For that reason, this article shouldn't be read as a benchmark.

Instead, think of it as the first experiment in an ongoing series.

As we continue exploring AI Search, we'll keep asking the same questions to different models, comparing not only the answers they produce but also the assumptions they make and the reasoning they use.

Sometimes the most interesting discovery isn't which AI gives the "best" answer.

It's seeing how different AI systems think about exactly the same problem.

Editor's Note

The scores in this article reflect our subjective evaluation of this single prompt only. They are not an overall ranking of the models, and different prompts may lead to very different outcomes.

Tags

AI Comparison, ChatGPT, Gemini, Perplexity, Ollama, Qwen, Gemma, Llama, AI Search, Google AI Overview, LLM, NetContentSEO, AI Labs