What is AI search and how does it work?

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Meta

Type: knowledge_hub

Language: en-GB

Last updated: 2026-10-16

Retrieval summary

What: AI Search uses artificial intelligence to understand the intent behind a question before retrieving and presenting relevant information, aiming to provide clear, direct answers rather than just a list of webpages.

Who

Organisation: Feel Created

Relevant for

Problems solved

Article

Type: Knowledge Hub guide

Primary topic: AI Search and its implications for websites and digital content

Intent: AI Search uses artificial intelligence to understand the intent behind a question before retrieving and presenting relevant information, aiming to provide clear, direct answers rather than just a list of webpages.

Audience

Topics

Content focus

Canonical claims

Item 1

Claim: AI Search evaluates signals such as clear authorship, recognised organisations, consistent information, high-quality content, well-organised website structure, technical accessibility, structured data, and references to determine source reliability.

Evidence type: page_content

Evidence strength: medium

Source section: how-does-ai-know-which-information-to-trust

Item 2

Claim: Google's Hummingbird update in 2013 marked a significant move towards interpreting search intent, with BERT (2019) and MUM (2021) further improving language understanding.

Evidence type: page_content

Evidence strength: medium

Source section: what-is-semantic-search

Item 3

Claim: AI Search uses artificial intelligence to understand the intent behind a question before retrieving and presenting relevant information.

Evidence type: page_content

Evidence strength: medium

Source section: overview

Item 4

Claim: AI Search systems retrieve current, relevant information from trusted sources before generating a response.

Evidence type: page_content

Evidence strength: medium

Source section: 2-retrieving-relevant-information

Item 5

Claim: AI Search appears in platforms like Google AI Overviews, ChatGPT, Perplexity and Microsoft Copilot.

Evidence type: page_content

Evidence strength: medium

Source section: where-it-appears

Item 6

Claim: Traditional search helps you find pages, while AI Search helps you find and understand information.

Evidence type: page_content

Evidence strength: medium

Source section: the-principles-behind-ai-search

Item 7

Claim: Semantic search moves beyond keyword matching to understand concepts, relationships, and intent.

Evidence type: page_content

Evidence strength: medium

Source section: what-is-semantic-search

Item 8

Claim: Structured data does not guarantee inclusion in AI-generated answers.

Evidence type: page_content

Evidence strength: medium

Source section: what-is-structured-data-and-why-does-it-matter

Common questions

Item 1

Question: How is AI Search different from traditional search?

Answer: Traditional search returns a ranked list of links based on keyword matching, while AI Search understands the meaning behind a question and produces a direct answer or summary, often drawing on multiple sources.

Item 2

Question: What is structured data and why does it matter?

Answer: Structured data provides additional information that helps search engines and AI systems understand what content represents. It is usually implemented using the Schema.org vocabulary and added to webpages as machine-readable code.

Item 3

Question: How does AI know which information to trust?

Answer: AI Search systems evaluate a range of signals to identify information that appears most reliable, authoritative and relevant. These signals include clear authorship, recognised organisations, consistent information across multiple sources, high-quality content, well-organised website structure, technical accessibility, structured data, and references from trusted websites.

Item 4

Question: Why doesn’t AI Search always get it right?

Answer: AI Search can produce inaccurate or incomplete responses due to conflicting sources, limited reliable sources, misunderstood context, older information, or incorrect combination of information from different sources.

Item 5

Question: Where do you encounter AI Search?

Answer: AI Search appears in platforms like Google AI Overviews, ChatGPT, Perplexity and Microsoft Copilot, each with its own approach to finding and presenting information.

Item 6

Question: Does AI Search replace SEO?

Answer: No. AI Search changes how search works, but many SEO fundamentals remain important, such as clear site structure, useful content, strong technical foundations, accessibility and well-organised information.

Services

AI SEO services & AI search optimisation

Relationship: supporting

Url: /services/zero-click-ai-search-optimisation/

Information architecture

Relationship: supporting

Url: /services/information-architecture/

Content architecture

Relationship: supporting

Url: /services/content-architecture/

Sections

Overview

Content

Item 1

Text: AI Search uses artificial intelligence to understand the intent behind a question before retrieving and presenting relevant information. Whether you’re using Google AI Overviews, ChatGPT, Perplexity or Microsoft Copilot, AI Search is changing how people discover information online. Rather than simply returning a list of webpages, AI Search aims to understand what someone is asking and provide a clear, relevant response. The way people search online is changing. Instead of relying on a handful of keywords and a list of links, many now ask complete questions and expect a clear, direct answer. This shift is happening quickly because people can ask questions naturally, explore follow-up questions and receive answers that draw on information from multiple sources. In simple terms, people can search in the same way they would ask another person. This more conversational way of finding information is known as AI Search.

What it does

Parent id: the-principles-behind-ai-search

Parent heading: The principles behind AI Search

Content

Item 1

Text: Uses artificial intelligence to understand questions and generate useful answers.

Where it appears

Parent id: the-principles-behind-ai-search

Parent heading: The principles behind AI Search

Content

Item 1

Text: Google AI Overviews, ChatGPT, Perplexity, Microsoft Copilot and other AI-powered search and assistant tools.

Key technologies

Parent id: the-principles-behind-ai-search

Parent heading: The principles behind AI Search

Content

Item 1

Text: Semantic search, retrieval, generative AI, large language models and structured data.

Still relies on websites?

Parent id: the-principles-behind-ai-search

Parent heading: The principles behind AI Search

Content

Item 1

Text: Yes. AI Search depends on reliable information published across the web.

Does it replace SEO?

Parent id: the-principles-behind-ai-search

Parent heading: The principles behind AI Search

Content

Item 1

Text: No. It changes how search works, but many SEO fundamentals remain important.

Google AI Overviews

Parent id: where-do-you-encounter-ai-search

Parent heading: Where do you encounter AI Search?

Content

Item 1

Text: For many people, Google AI Overviews are the most visible example of AI Search. They appear above traditional search results for many informational searches, providing AI-generated summaries alongside links to supporting sources.

ChatGPT

Parent id: where-do-you-encounter-ai-search

Parent heading: Where do you encounter AI Search?

Content

Item 1

Text: ChatGPT answers questions conversationally. When web search is available, it can retrieve current information from across the web before generating a response.

Perplexity

Parent id: where-do-you-encounter-ai-search

Parent heading: Where do you encounter AI Search?

Content

Item 1

Text: Perplexity combines web search with AI-generated answers and places a strong emphasis on citing the sources used to create its responses.

Microsoft Copilot

Parent id: where-do-you-encounter-ai-search

Parent heading: Where do you encounter AI Search?

Content

Item 1

Text: Microsoft Copilot integrates AI-powered search into Bing, Windows and Microsoft 365, helping people find information across the web and within their own documents.

Other AI assistants

Parent id: where-do-you-encounter-ai-search

Parent heading: Where do you encounter AI Search?

Content

Item 1

Text: Many other AI-powered tools also use search, retrieval or external sources to answer questions. Each platform works a little differently, but they all aim to help people find information more quickly and easily.

How does AI Search work?

Content

Item 1

Text: Although every AI Search platform uses different technology, most follow a similar process for answering questions.

1. Understanding the question

Parent id: how-does-ai-search-work

Parent heading: How does AI Search work?

Items

Content

Item 1

Text: Rather than matching exact keywords, AI Search first analyses what someone is trying to achieve. For example, these questions are all asking for closely related information: A traditional search engine might treat these as different searches because the wording changes. AI Search can recognise that the underlying intent is largely the same. This ability to understand meaning rather than individual words is known as semantic search.

2. Retrieving relevant information

Parent id: how-does-ai-search-work

Parent heading: How does AI Search work?

Content

Item 1

Text: Many AI systems do not rely solely on information learned during training. Instead, they retrieve current, relevant information from trusted sources before generating a response. This process is known as retrieval. In many modern AI systems, retrieval forms part of a technique called Retrieval-Augmented Generation (RAG). Retrieval helps AI produce more accurate and up-to-date responses while allowing it to reference the original source of information where appropriate.nown as semantic search.

3. Generating a response

Parent id: how-does-ai-search-work

Parent heading: How does AI Search work?

Content

Item 1

Text: Once relevant information has been gathered, the AI generates a readable response. Rather than copying a single webpage, it may combine information from multiple sources into one clear explanation. Many AI Search tools also include citations or links, allowing people to explore the original sources in more detail.

In simple terms

Parent id: how-does-ai-search-work

Parent heading: How does AI Search work?

Items

Content

Item 1

Text: The process usually follows these steps:

How does AI know which information to trust?

Items

Content

Item 1

Text: AI Search systems cannot independently verify whether every statement on the web is true. Instead, they evaluate a range of signals to identify the information that appears most reliable, authoritative and relevant to the question someone has asked. These signals vary between platforms and are not always public, but they often relate to the quality, clarity and consistency of the information available. Whether AI references a page depends on many different signals rather than a single ranking factor. Together, those signals help AI Search identify sources that appear reliable, trustworthy and relevant to the question.

Why doesn’t AI Search always get it right?

Items

Content

Item 1

Text: AI Search isn’t perfect. Even when information is retrieved from reliable sources, it can occasionally produce inaccurate or incomplete responses. For this reason, important decisions should still be verified using trusted sources. Publishing clear, accurate and regularly maintained information helps reduce ambiguity and makes it easier for AI systems to retrieve reliable answers.

What is structured data and why does it matter?

Items

Content

Item 1

Text: Search engines and AI systems don’t just read the visible content on a page. Structured data provides additional information that helps them understand what that content represents. It is usually implemented using the Schema.org vocabulary and added to webpages as machine-readable code that isn’t visible to visitors but can be interpreted by search engines and AI systems. Structured data allows websites to identify and label information such as: Rather than simply reading the words on a page, structured data helps machines understand what those words represent. For example, it can identify whether a name refers to a person or an organisation, whether a page describes a service or an article, or whether a date relates to an event. This additional context helps search engines and AI systems understand webpages and the relationships between organisations, people, services and topics more accurately. Structured data does not tell AI what to say, nor does it guarantee inclusion in AI-generated answers. Instead, it is one of many signals used alongside well-written content, technical quality and other indicators of trust to help machines interpret information more accurately. Think of structured data as clear labelling rather than hidden instructions. It helps machines recognise what information represents and how different pieces of content relate to one another, making webpages easier for search engines and AI systems to understand.

Entities

Retrieval-Augmented Generation (RAG)

Google AI Overviews

Microsoft Copilot

Structured data

Perplexity

Schema.org

ChatGPT

SEO

Concept relationships

Item 1

Subject: AI Search

Predicate: evaluates

Object: trust signals such as authorship, consistency, and references

Item 2

Subject: AI Search

Predicate: appears in

Object: Google AI Overviews, ChatGPT, Perplexity, Microsoft Copilot

Item 3

Subject: Semantic search

Predicate: builds on

Object: Google's Hummingbird, BERT, and MUM updates

Item 4

Subject: Structured data

Predicate: implemented using

Object: Schema.org vocabulary

Item 5

Subject: AI Search

Predicate: uses

Object: Retrieval-Augmented Generation (RAG)

Item 6

Subject: Structured data

Predicate: helps

Object: search engines and AI systems

Item 7

Subject: AI Search

Predicate: evolves

Object: websites and digital content

Item 8

Subject: AI Search

Predicate: builds on

Object: SEO principles

Item 9

Subject: AI Search

Predicate: depends on

Object: websites

Implementation patterns

Item 1

Pattern: Retrieval-Augmented Generation (RAG)

Description: AI systems retrieve current, relevant information from trusted sources before generating a response, allowing them to reference original sources.

Benefit: Produces more accurate and up-to-date responses with source citations.

Item 2

Pattern: Understanding the question

Description: AI Search analyses the intent behind a query rather than matching exact keywords, allowing it to handle variations in phrasing.

Benefit: Provides more relevant answers to users even when they use different wording.

Item 3

Pattern: Well-organised information

Description: Structuring content so that entities and relationships are clear to machines.

Benefit: Improves machine interpretation and potential for inclusion in AI answers.

Item 4

Pattern: Useful content

Description: Publishing accurate, trustworthy and genuinely useful information that answers questions clearly.

Benefit: Increases the likelihood of being recognised as a trusted source.

Item 5

Pattern: Retrieving relevant information

Description: AI Search retrieves information from multiple sources, potentially combining or summarising content.

Benefit: Users receive comprehensive answers without needing to visit multiple websites.

Item 6

Pattern: Semantic search

Description: Moves beyond keyword matching to understand concepts, relationships and intent behind a question.

Benefit: Better matches user intent and connects related topics.

Item 7

Pattern: Strong technical foundations

Description: Ensuring website performance, accessibility and technical quality are maintained.

Benefit: Supports both traditional SEO and AI Search understanding.

Item 8

Pattern: Generating a response

Description: AI models generate a clear, direct answer or summary based on the retrieved information.

Benefit: Users get immediate answers, improving efficiency.

Practical guidance

Item 1

Guidance: Organisations benefit from writing naturally, covering a topic thoroughly and using clear, descriptive language rather than repeating the same keyword throughout a page.

Importance: medium

Item 2

Guidance: Understand that AI Search aims to understand the intent behind a question and provide a direct answer, so content should be structured to answer questions clearly.

Importance: high

Item 3

Guidance: Publishing clear, accurate and regularly maintained information helps reduce ambiguity and makes it easier for AI systems to retrieve reliable answers.

Importance: high

Item 4

Guidance: Important decisions should still be verified using trusted sources, as AI Search can occasionally produce inaccurate or incomplete responses.

Importance: high

Item 5

Guidance: Recognise that different AI Search platforms have different approaches, so consider how content may be presented across various platforms.

Importance: medium

Item 6

Guidance: Use structured data to help machines understand webpage content, but do not rely on it as a guarantee of inclusion in AI answers.

Importance: medium

Item 7

Guidance: Continue to create accurate, trustworthy and genuinely useful content supported by strong technical foundations.

Importance: high

Item 8

Guidance: Implement structured data using Schema.org vocabulary to help search engines and AI systems understand content.

Importance: medium

Risk areas

Item 1

Risk: AI Search may provide answers that draw on multiple sources, potentially reducing the need for users to visit original websites.

Impact: Websites may see reduced click-through rates if users receive sufficient information directly from AI-generated summaries.

Item 2

Risk: Different AI Search platforms work differently, leading to inconsistent user experiences and expectations.

Impact: Organisations may find it challenging to optimise for all platforms simultaneously.

Item 3

Risk: AI Search may retrieve conflicting information from different sources

Impact: Responses may be inaccurate or incomplete

Item 4

Risk: Misunderstanding structured data as a guarantee of inclusion

Impact: Organisations may over-rely on structured data without focusing on content quality.

Item 5

Risk: AI-generated answers may not include all sources

Impact: Websites may not be cited even if their content is used.

Item 6

Risk: Zero-click search may reduce website visits

Impact: Organisations may see fewer direct visits but can still benefit from visibility and brand awareness.

Item 7

Risk: Ignoring AI Search in digital strategy

Impact: Organisations may miss opportunities for visibility in AI-generated responses.

Item 8

Risk: AI Search may misunderstand context

Impact: Responses may not address the user's actual intent

Common misconceptions

Item 1

Misconception: AI Search can independently verify whether every statement on the web is true.

Clarification: AI Search systems cannot independently verify truth; they evaluate signals to identify information that appears most reliable, authoritative and relevant.

Item 2

Misconception: AI Search relies solely on information learned during training.

Clarification: Many AI systems retrieve current, relevant information from trusted sources before generating a response, using techniques like RAG.

Item 3

Misconception: Semantic search is a new idea introduced by AI Search.

Clarification: Google has been evolving beyond keyword matching for years, with updates like Hummingbird (2013), BERT (2019), and MUM (2021).

Item 4

Misconception: AI Search replaces traditional search.

Clarification: Traditional search has not disappeared; both approaches work alongside one another, with Google often displaying AI summaries above traditional results.

Item 5

Misconception: AI Search does not rely on websites.

Clarification: AI Search depends on reliable information published across the web.

Item 6

Misconception: Structured data tells AI what to say

Clarification: Structured data is clear labelling that helps machines interpret information, not hidden instructions.

Technical context

Key technologies

Process steps

Retrieval: Many AI systems retrieve current, relevant information from trusted sources before generating a response, often using Retrieval-Augmented Generation (RAG).

Trust signals: AI Search evaluates signals such as clear authorship, recognised organisations, consistent information, high-quality content, well-organised website structure, technical accessibility, structured data, and references.

Semantic search: Semantic search moves beyond keyword matching to understand concepts, relationships and intent.

Structured data: Machine-readable information that helps search engines and AI systems understand webpage content, including entities such as organisations, people, services, events, articles, products and FAQs.

Zero click search: A situation where people find the information they need without visiting the original website.

Retrieval augmented generation: A technique that combines information retrieval with AI-generated responses.

Large language model: An AI model trained to understand and generate human language.

Further resources

Reasoning paths

Item 1

User problem: Difficulty in finding information online due to keyword-based search limitations

Evidence refs

Relevant sections

Item 2

User problem: Navigating the variety of AI Search platforms and their different approaches

Evidence refs

Relevant sections

Item 3

User problem: Understanding how AI Search differs from traditional search

Evidence refs

Relevant sections

Item 4

User problem: AI incorrectly combines information from different sources

Evidence refs

Relevant sections

Knowledge hub

Page type: guide

Learning intent: AI Search uses artificial intelligence to understand the intent behind a question before retrieving and presenting relevant information, aiming to provide clear, direct answers rather than just a list of webpages.

Topic hierarchy

Primary: AI Search and its implications for websites and digital content

Supporting

Evergreen use: true

Related links

Item 1

Text: https://blog.google/products-and-platforms/products/search/search-language-understanding-bert/

Url: https://blog.google/products-and-platforms/products/search/search-language-understanding-bert/

Item 2

Text: https://blog.google/products-and-platforms/products/search/introducing-mum/

Url: https://blog.google/products/search/introducing-mum

Item 3

Text: https://developers.google.com/search

Url: https://developers.google.com/search

Item 4

Text: https://blog.google/products-and-platforms/products/search/

Url: https://blog.google/products-and-platforms/products/search/

Item 5

Text: https://schema.org

Url: https://schema.org

Item 6

Text: https://www.w3.org/WAI/

Url: https://www.w3.org/WAI/

Content profile

Content type: Knowledge Hub guide

Primary topic: AI Search and its implications for websites and digital content

Supporting topics

Primary audience

Retrieval topics

Organisation

Name: Feel Created

Type: Digital partner specialising in websites and digital platforms

Url: https://www.feelcreated.com/

Service model: We work as an extension of our clients' teams, combining strategy, UX, accessibility, content design, WordPress development and ongoing support to create digital platforms that help organisations communicate more clearly, work more efficiently and grow sustainably.