What is conversational search?
Conversational search is a form of information retrieval in which users phrase their query as a question in natural language and the search system keeps track of the context across several rounds of conversation. Instead of a list of links, conversational search delivers a written answer and responds to follow-up questions without the query having to be entered again. Well-known examples are AI Mode in Google Search, search in ChatGPT, Perplexity, and Microsoft Copilot.
Term profile
| Attribute | Details |
|---|---|
| Part of speech | Noun, technical term |
| Pronunciation | ˌkɑːnvərˈseɪʃənəl sɜːrtʃ |
| Also known as | Dialog-based search, conversational information seeking |
| Opposite | Keyword search with search terms and a results list |
| Well-known systems | Google AI Mode, ChatGPT search, Perplexity, Microsoft Copilot, voice assistants |
| Related terms | AI search, query fan-out, retrieval-augmented generation, voice search, search intent |
How does conversational search work?
Conversational search works in 4 steps: The system understands the question, keeps the course of the conversation, puts together an answer from several sources, and invites the next question.
- Understanding the question: The system reads the input as a whole sentence and recognizes the intention behind it, in other words the search intent. The basis for this is natural language processing.
- Keeping the context: Every new question is read in light of the previous ones. If someone asks about a bakery and then asks “Is it open on Sundays too?”, the system knows that “it” refers to the bakery mentioned before.
- Putting the answer together: The system breaks the question down into several sub-searches, a process called query fan-out. A language model summarizes the passages it finds into an answer and links the sources. This coupling of search and language model is called retrieval-augmented generation.
- Moving the conversation forward: At the end, the system often suggests suitable follow-up questions or asks a clarifying question itself if the query was ambiguous.
The decisive difference lies in the second step. A classic search engine treats every input as a new, independent query. Conversational search treats the inputs as one connected conversation in which every answer builds on what came before.
How does conversational search differ from classic keyword search?
Conversational search differs from keyword search in input, context, result, and measure of success. The following table compares the two forms:
| Attribute | Keyword search | Conversational search |
|---|---|---|
| Input | A few search terms, such as “bakery schwabing” | Whole sentences and questions in everyday language |
| Context | Every search starts from scratch | The course of the conversation is retained |
| Result | List of links and snippets | Written answer with source links |
| Follow-up questions | Not provided for | Follow-up questions and clarifying questions from the system |
| Role of the user | Chooses from the results themselves | Lets the system choose and summarize |
| Success for websites | Position in the results list and a click | Being mentioned or linked as a source in the answer |
Both forms exist side by side. Many users ask a question in a dialog and switch back to classic search for a specific website or product. Anyone who serves only one of the two forms loses part of the demand.
When did conversational search start?
The idea of dialog-based search is older than ChatGPT and has developed over more than ten years in 3 stages.
- Voice assistants (from 2011): Apple introduced Siri in 2011, and Google followed in 2012 with Google Now. The assistants understood spoken questions but could hardly hold several rounds of conversation.
- Semantic search (from 2013): In 2013 Google itself called spoken search in Chrome conversational search. In the same year, the Hummingbird update put the meaning of whole questions at the center of semantic search.
- AI search with language models (from 2022): Perplexity launched in December 2022, and Microsoft released Bing Chat in February 2023, which is now called Copilot. OpenAI added its own web search to ChatGPT in October 2024. Google launched AI Mode in the US in 2025, and it has also been available in Germany since October 8, 2025.
In parallel, research has studied the field systematically. The Text Retrieval Conference program of the US National Institute of Standards and Technology (NIST) ran its own competition for dialog-based search systems from 2019 to 2022, the Conversational Assistance Track.
Which systems offer conversational search?
In German-speaking markets, 5 systems shape conversational search.
| System | Provider | Distinctive feature |
|---|---|---|
| AI Mode | Separate dialog mode in Google Search, available in Germany since October 2025 | |
| ChatGPT search | OpenAI | Web search inside ChatGPT with source links |
| Perplexity | Perplexity AI | Answer engine that backs every statement with numbered sources |
| Copilot | Microsoft | Dialog search based on Bing, built into Windows and Office |
| Voice assistants | Apple, Amazon, Google | Siri, Alexa, and Gemini answer spoken questions in a dialog |
AI Mode is particularly important for websites because it sits directly in the Google search box. The guide to Google AI Mode goes deeper into what the mode means for clicks and visibility. The article on AI search gives an overview of all systems that deliver answers instead of results lists.
What does conversational search mean for SEO?
Conversational search shifts the goal of SEO: A page not only has to be found but has to fit into an answer as a source. This results in 5 tasks for content:
- Anticipating the next question: A conversation rarely ends with the first question. Anyone who asks about a service will then ask about costs, duration, and process. A page that answers these follow-up questions stays relevant as a source across several rounds.
- Putting the answer first: The first sentence of a section should answer the question in its heading directly. That way the system can extract the statement cleanly.
- Writing self-contained sections: With query fan-out, often only a single section ends up in the answer. It should make sense without the rest of the page.
- Researching in questions instead of keywords: The queries in Google Search Console increasingly show whole sentences. A filter on question words such as “how”, “which”, or “how much does” reveals which conversational questions users really ask.
- Measuring visibility in answers: Rankings alone do not show whether a brand appears in answers. A more meaningful metric is share of voice in AI answers, measured with fixed questions across several systems.
The spoken version of search requires the same, just without a screen. The article on voice SEO describes what also matters there. Our page on answer engine optimization (AEO, also known as GEO) and AI visibility shows how content can be aligned specifically with AI answers.
Frequently asked questions about conversational search
Is conversational search the same as voice search?
No. Voice search describes the input channel by voice, conversational search describes the dialog across several rounds. The two often overlap, but conversational search works just as well with typed input.
What is the difference between conversational search and a chatbot?
A classic chatbot answers from a fixed knowledge base or from predefined answers. Conversational search searches an index of web pages or documents for every question and names the sources the answer comes from.
Will conversational search replace classic Google Search?
No. The classic results list continues to exist, and conversational search is added as an additional route. Google offers AI Mode as a separate area alongside the normal search results.
