When we covered AI search last year, there was still much debate about whether or not website managers should block AI bots from crawling their content (and thus, attempt to block the LLMs from using their content in AIgenerated responses) or whether they should “welcome our new AI overlords” and try to position their businesses for more inclusion in the responses generated by the likes of ChatGPT for greater brand visibility.
This year, it seems more digital marketing and search professionals have erred on the side of inclusion and optimizing for brand visibility in generative AI. As more AI tools have evolved to include citations or links to original sources by default (a core difference between Perplexity or SearchGPT and ChatGPT), the hesitance around allowing AI bots to crawl websites has diminished somewhat — especially as Google itself has integrated AI-augmented SERP features like AI Overviews into its core search offering.
AI SEO acronyms: AIO? GEO? CEO? LMO?
How should we refer to optimizations aimed at getting your brand mentioned in LLMs, AI search engines, and AI-augmented SERP features?
There doesn’t yet seem to be a consensus among SEO and digital marketing professionals, though we’ve seen the following terms used within the industry:
- AIO: AI Optimization
- GEO: Generative Engine Optimization
- CEO: Chat Engine Optimization
- LMO: Language Model Optimization
For this guide, we’ll use the terms ‘AIO’ and ‘GEO’ interchangeably to cover optimizations for both LLM inclusion and AI-augmented search visibility.
What’s the difference? LLMs vs. AI-augmented search
LLM Search
LLM platforms are sometimes used for search, though they were not developed as search engines.
Not all LLMs will include citations or links in their AI-generated responses by default. This means that, depending on the platform, you may or may not receive a link in the AI-generated responses, even if the model has been trained on your content.
Often, these AI tools work off of static data — that is, they have an information cut-off date —and may be unable to provide the most up-to-date information in their replies.
LLM Examples:
- ChatGPT
- Google Gemini
AI-Augmented Search
AI-augmented search refers to AI search tools or SERP features that are purpose-built for online search. These AI-powered search result summaries include citations/links to original sources by default.
These AI tools have real-time access to information (via pages currently indexed in search engines) and can provide more up-to-date information compared to LLMs that work off of static data with a knowledge cut-off date.
AI-Augmented Search Examples:
- Google’s AI Overviews
- Bing’s AI Summaries
- SearchGPT
- Perplexity
It’s worth noting the differences that exist between AI or LLM search and AI-augmented search and SERP features.
As Crystal Carter, Head of SEO Communications at Wix, pointed out at BrightonSEO last year, LLMs used for search are not the same as, say, Google’s AI Overviews that appear in search (or Bing’s incorporation of AI summaries in search).
Using ChatGPT, which was not built specifically for search purposes, as a search engine could be considered AI/LLM search — while Google’s AI Overviews, a purpose-built SERP feature, falls under AI-augmented search.
The difference largely comes down to user journeys. The user journey for ‘searchers’ using LLM tools like ChatGPT is notably different from the user journey on traditional search engines like Google. It’s also distinct from users’ interactions with Google’s AI Overviews and other SERP-integrated AI features. This is because users simply “stumble upon” Google’s AI Overviews by following their usual search behaviors — as opposed to specifically navigating to an LLM or generative AI tool like ChatGPT for a more conversational approach to their queries.
People using LLMs for search are after a more interactive search experience. They can follow up on their original queries in a conversational manner that s quite different from the user experience on traditional Google search. Even with Google’s AI Overviews, users simply receive the information rather than conducting an ongoing conversation as they would with an AI chatbot. There is no option for back-and-forth conversation in Google’s AI Overviews because they are simply an added feature on top of the traditional search results.
Another key difference is that AI-augmented search is purpose-built for online search — and generally can provide more up-to-date information because these tools do not suffer from an information cut-off date, as is the case with some LLMs.
Example outputs: LLM search vs. AI-augmented search
As an illustration, let’s look at the search results for the same query across both LLM platforms and AI-augmented search platforms. In this example, let’s use the search term:
- “Where is the best place to buy new running shoes?”
LLM search results
ChatGPT provides the following response to our query about where to buy new running shoes — note the lack of citations or links:
AI-augmented search results
SearchGPT provides a similar response, but with the inclusion of links:
Also on the AI-augmented side of search, Perplexity (which bills itself as an ‘AI search engine’) returns another response with citations/links provided by default:
A SearchGPT response with no links included
Note, however, that despite being billed as ‘for search’, SearchGPT does not always include citations or links! When asked a broader, more philosophical question, SearchGPT returned a citation-free, link-free response, similar to what you’d get on standard ChatGPT:
Google AI Overviews
At the time of writing, the same query, “what is the best place to buy new running shoes?” does not return an AI Overview in traditional Google search.
A related but less commercially-focused query on trail running, however, does return an AI Overview, with links to its original sources.
As a relatively new SERP feature, Google’s AI Overviews do not appear on every query. The percent of queries that do return an AI Overview also has changed month to month, with Search Engine Land reporting AI Overviews showing up on a massive 84% of Google queries back in December 2023 (when this feature was still known as ‘SGE’, or ‘Search Generative Experience’) — later falling to 7% of queries in July 2024. As Google continues to refine its AI output, the number of queries that return AI Overviews may well grow again.
When AI Overviews do appear on Google SERPs, however, they occupy a substantial portion of the page.
The move toward more platforms deciding to include citations and links in their AI-augmented search tools is likely a large part of why more brands have begun to consider optimizing their content for inclusion in these AI-generated responses — instead of trying to block AI bots from crawling their web content entirely.
“AI-driven summaries now occupy a substantial portion of the SERP — on some queries, AI Overviews can take up nearly half of the visible search results page.
This shift significantly impacts user behavior, as users are less likely to scroll past these prominent Generative AI results to reach traditional organic listings further down the page.
Consequently, achieving visibility within these AI-generated summaries will become increasingly critical for maintaining a strong online presence, capturing user attention, and driving traffic.”
How to optimize your brand for visibility in AI search
“SEO professionals should start building strong connections with branding departments now, as collaboration with them will become increasingly vital to brand positioning in generative AI-driven search.”
— Sara Moccand-Sayegh, SEO Team Lead @ BlueGlass
Branding is key to generative search; a well-defined brand is more accurately recognized by Google and generative models, resulting in more relevant outcomes.
Fabrice Canel demonstrated that users increasingly interact with chatbots by asking informational queries like “How…” or commercial navigational queries like “Best…” In both cases, it’s crucial to ensure your brand is part of the answer. Chatbots like ChatGPT, which reference sources, present new opportunities to drive traffic to your site by embedding your brand in these responses.
Six steps to successfully position a brand for generative AI:
- Thorough research: Collaborate with the brand manager to gather all essential brand information, including market positioning and target audience.
- Consistent messaging: Consistent communication is vital! Even minor inconsistencies can impact brand recognition and create confusion for Google and AI systems.
- Trustworthy website: A credible website with relevant content and clearly structured “About Us” and “Policy” pages strengthens brand image and reinforces trust.
- Building brand authority: Recognition in your field, such as through awards, enhances market position and strengthens brand authority.
- Establish your brand as an entity: By positioning your brand as an entity in Google’s Knowledge Graph, you automatically support generative chatbots. First, by establishing entity status, you enhance clarity around your brand’s identity, which is essential for machines to understand it accurately. Second, as Retrieval-Augmented Generation (RAG) becomes more popular, there is a greater chance that LLM systems interact with other technologies, like the Knowledge Graph, to access factual information about your brand, leading to more accurate responses.
- Brand understanding across the customer journey: Content should be tailored to each stage of the customer journey to ensure users receive relevant information and fully understand your brand. Additionally, producing content and feeding it directly to AI systems reduces the risk of misinformation or “hallucination.”
Knowledge graph optimizations for AI search
[From: On-Demand Webinar: AI, Knowledge Graphs, & SEO: Teaching LLMs About Your Business ]
One potential way to improve visibility in Google’s AI search tools (and to counter AI search hallucinations) centers on ensuring that Google’s Knowledge Graph — the database that stores information about entities, people, or businesses and represents it in a quick-to-process way for machines — is supplied with up-to-date, factual information. But it is not always easy to do this.
SEO specialist Sara Moccand points to a 2022 paper on Google’s LaMBDA project (part of its AI development efforts) entitled “LaMBDA: Language Models for Dialog Applications.”
“It really, really, really, really… looks like they are combining Google AI search features with the Knowledge Graph,” Moccand says of the paper’s content in her Lumar webinar session on AI and SEO.
Assuming Google’s AI search tools are pulling information from its Knowledge Graph database when generating responses for users, Moccand recommends optimizing your business’s content for inclusion in Google’s Knowledge Graph to help boost brand visibility and combat any potential hallucinations AI bots may generate about your company.
So, with a changing search landscape to contend with, how can SEO professionals stay ahead of the curve in 2025?
To dive deeper into AI search, AI-augmented search, and the general state of SEO this year, download our full eBook on 2025 SEO Trends.
