June 2026 Update:
It’s been a full year since we originally published this blog post dedicated to understanding AI Search Engine Terminology. Since this area continues to evolve rapidly, we felt it was time for an update! Any Text in GREEN below reflects information that was added to the post since we first wrote it.
A big challenge to all marketers today is the number of different terms that are being thrown around related to the use of AI search engines, or what can be called “answer engines.” These are also referred to as LLMs or LLM engines since they are powered on large language models. In addition, these terms aren’t always used consistently and mean different things to different organizations and marketers. To inform our readers about the current AI search engine landscape and help them understand what certain terms truly mean, we’ve put together the below glossary of AI search terms.
AI Search Glossary
- AEO – while this acronym is also used for non-marketing and non-search-engine-related topics, when applied to search engine optimization (SEO), it’s related to one of the two broad branches of search tools (answer engines) that are available for use by searchers today, and stands for “Answer Engine Optimization.” Explained in this outstanding Better Business Bureau blog post about AEO, AEO practices and tactics focus on creating and sharing content that provides a “direct, informative, and well-structured answer” to a question that a searcher enters into a generative AI interactive, conversational (chat) tool, such as Perplexity, ChatGPT, and Google AI Mode. As we explained in our blog post about generative AI, the whole premise behind AEO is to answer questions searchers have via the online content your organization produces and shares. Don’t lose site of the variety of content AI search engines may pull and provide to question askers — check out our comprehensive list!
- Answer Engines – as alluded to above, “answer engine” is a term used to describe interactive, conversational tools like Perplexity, ChatGPT, Google AI Mode, and Google Gemini. In lieu of spitting back a long list of links to websites and other online/digital properties (known as SERPs), these answer engines tend to provide content blocks — often without links — that synthesize data from a number of websites to answer the searcher’s question. This can mean that your organization or your content may be mentioned/included in response to an appropriate “prompt,” but a link back to your website, known as a citation won’t always be included.
- Agentic AI – it’s not yet known what the implications are related to traditional search engines and answer engines, but in the words of Nvidia, agentic AI refers to AI that “uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.” As of the date of the revisions to the blog post, many SEO experts are using this term to pertain to a tool that individuals sign up to use that allows the tool to choose and/or buy a product or service on behalf of the tool user.
- AI Search – this is one of the terms that we were thinking of when we mentioned that different organizations and marketers are using this term differently since AI search can refer to the use of an answer engine like the ones we discussed above to identify external resources and external data related to a particular question, or it can be used to describe searches of organization-owned/internal data (this data may extend to data that the organization shares publicly or enters into digital tools or apps or technology, but ultimately, it’s information owned by or generated by the organization.) An example of this latter case is discussed in this guest blog post by Medullar who offers an AI productivity and collaboration tool for SMBs, and particularly consulting firms and professional-services firms.
- Chat Search Engine/Chatbot Search Engine – chat search engines/chatbot search engines really are the same thing as answer engines (described above). They employ the conversational, interactive capability of chatbots, but also the capabilities of providing answers to questions from data they collect/gather across the internet the way that traditional search engines like Google, Bing, Yahoo, and DuckDuckGo do. It’s basically a search engine that users chatbot functionality to interact with individuals asking questions vs. just searching using a statement, term, or topic.
- Chatbot – technology that provides an interface for humans to communicate with that interface via voice commands, text (typed commands) or both. It is considered a form of AI and is employed in online messaging or online chat services used by organizations to answer customer or prospective customer questions like LiveChat ones.
- Generative AI – generative AI is technology that pulls data/content from a variety of sources and produces (generates!) new content based on what it’s learned. The content can be viewed as a summary or synthesis of information collected by the technology.
- GEO (Generative AI Optimization) – employing best practices and tactics so that your organization appears in generative AI results; basically, same as AEO with the nuance that instead of answer/AI search engines serving up verbatim the answer that you provide to a question, answer/AI search engines are “citing” your organization because they view you as an expert and are providing a link back to your website as part of their results to a relevant “prompt.”
- Large Language Models (LLMs) – if you really want to get into the nitty-gritty of understanding how LLMs work, Search Engine Land explains it well. Most organizations/lay people simply need to have a general understanding of this term because it’s often used in regards to answer engines/chatbot engines/generative AI engines because it’s the LLM technology/capability that lets the engines service engine users the way they do. As Search Engine Land states, “The core functionality of LLMs in search is to process queries and produce natural language summaries. Instead of just extracting information from existing documents, these models can generate comprehensive answers while maintaining accuracy and relevance. This is achieved through a unified framework that treats all (search-related) tasks as text generation problems. What makes this approach particularly powerful is its ability to customize answers through natural language prompts. The system first generates an initial set of query results, which the LLM refines and improves.”
- Mention – this is when the name of your organization gets served up in AI search engine results. This may happen with or without the engine including a link back to your website (citation).
- Prompt – this is what a user of an answer engine/AI search engine like ChatGPT or Google Gemini enters into the tool to get an answer/results/help/advice, etc. Check out this blog post about why appearing in AI search prompt results may help your organization.
- RAG (Retrieval-Augmented Generation) – refers to a content-generation method that takes advantage of both information information retrieval and LLM. It’s about producing content that’s both relevant and accurate by combining external data with generative AI capabilities.
Continue to Check Back For AI Search Engine Terminology Updates
We read, listen to, watch everything we can get our hands on these days related to changes in the world of search engine optimization and answer engine optimization. In keeping with the aforementioned, we will continue to update our terminology list, so please continue to check back!
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