Identify searches that need grounding to focus your AI strategy – with Mark Williams-Cook

Even if you're doing traditional SEO now, if you're thinking about AI Search, it's a multi-site approach so it's not just about getting your site to rank, it's about getting your service, your brand, your products, talked about in all of those results that are coming up and making sure you've got a good sentiment that describes what you're doing because that's what the AI is going to be filtering for. I'm Mark Williams Cook and this is SEO in 2026. Mark, what's your number one SEO tip for 2026? My tip is I think everyone should be determining whether their AI searches need grounding or not. Okay, now is this electrical or what kind of grounding are we talking about here? So every time you do a search on a large language model-based platform, so think about something like Google's AI mode, which is Gemini or ChatGPT by OpenAI, when you put a prompt in a query, whatever you want to call it, one of the first things that model does nowadays is determine if the query needs grounding and what grounding means is whether it needs to go and check its answer on the web for more recent information. So to give you an example, if you did a search for something like what do red blood cells do? It's highly unlikely that kind of query would need grounding because the answer to that query is not likely to change and it's not likely to need up to date information. And the reason the large language models know this is the way they work, which is this whole token prediction thing where they look at all the content on the web and they recognize patterns of content. They'll know when they see this kind of query, this pattern, there's a very kind of sharp bell curve in terms of there's lots of consistency in the answer it doesn't seem to change over time. So they can be fairly confident there's a good consensus and this is the correct answer. If you ask a question to give you the other extreme example, like what happened in today's news? If you imagine a large language model looking inside for those kind of tokens like what happened in the news today and then trying to figure out what comes next, it's going to look very different in terms of from a probability point of view. There isn't a clear. Well, this is what I need to say next because it's a topic that regularly changes and it needs outside information. So those kind of things will require what we call grounding, which is will go off and it will go and do web searches in the background to find information to answer that query for you. So what does this mean practically for an SEO wondering whether or not it's likely that the SEO is likely to be able to actually get a new up to date answer featured quickly with an AI search results because I would imagine that you would potentially want to target queries that had more rounding required. Yeah, absolutely. So the reason why I think this tip is important is if you are becoming focused on AI visibility, like lots of people are now knowing which queries are being grounded is giving you the inside track on which ones you can actually change in a reasonable time period to kind of describe it. So the example I gave earlier like about you know red blood cells, that's what I would define a solve the knowledge and the only way to really impact the answers there is to get into the training data of the model, which is a long process. It won't you won't see the impact of that until there's been a model update and because of the way it's fetching the information, it's it's quite a lot of work to change that answer. Conversely, if you know a query has a high chance of being grounded, I've done many demonstrations where we've impacted the result to the AI is generating within days. So it's really a massive time saver of OK, we know this is a topic we're going after and he is 500 kind of questions, but we know 200 of them, for instance, are ones that aren't particularly worth focusing on at least in the in the near term. So is it possible to define the probability of a phrase that requires a lot of grounding and therefore use that to assist with your deciding whether or not you're going to be targeting a particular keyword phrase and writing content for that. Yes, bang on so there's a few ways to actually do this if you're kind of techie actually every time you do a search on chat gbt, if you open up the developer console and go to network. You'll see in there there's a there's a panel for the conversation it's called conversation and it's it's got some of the kind of information that you're sending to chat gbt and some of the things it's doing in the background. And it actually generates a thing called search underscore prop, which is a number zero to one of the probability that query will require grounding and there is a threshold at which it will decide OK, I need to go and do a web search now that threshold depends on the model you use and even whether you're using a free or paid version of chat gbt, so I noticed the paid versions will be a lot more keen to do web searches I assume because it costs them money to go and do web searches. The probability thresholds around 0.65 for the free version of chat gbt, so anything above that you can be very sure that it's going to go and ground it. The other way you can do it if you're slightly less techie, but still a bit techie is there is actually a Google Gemini grounding API, which you can send queries to and it will tell you whether or not it will ground that query and really helpfully and it's kind of the next phase of this process is it will even tell you what web searches it's going to do in the background. So the example that they give in the Gemini documentation is if someone asks who wins the like Euro 2020 for whatever it says OK, I'm going to do these three web searches in the background to find this information now that does cost a little bit of money it's about three cents per search. So if you are not even that technical there's some very helpful community members so Dan Petrovic is one of them and he's released a couple of public models that you can use where you can just paste in lists of keywords and he's trained machine learning models on the output from chat gbt and Google Gemini and it can give you a fairly good prediction as to whether that query will be grounded or not. So if you just want a free way to do it, he's got a web interface you just paste in your 200 keywords it will run for a couple of minutes and it will say these ones are going to be grounded and these ones are not going to be grounded. So the Google Gemini grounding API certainly sounds interesting and you can obviously take a keyword phrase, take a prompt and determine whether or not something is going to require additional searches online or the AI to be comfortable that the information. Provided is most relevant for the user and you've obviously talked about a tool there that you can put a number of keyword phrases into to get some results for this. What about uncovering raises that you don't necessarily know about are there tools that you're aware of that you can actually use to uncover potential phrases that you haven't actually actively considered. Yeah, so this is really interesting right so this is the how the paradigm is shifted between traditional search and people using AI search because the traditional search paradigm is you do like these one shot searches and you use a few keywords and you go to Google and you search for one thing and then you decide what next thing you want to search for and some people have really good Google food, you know they just know what to search for to get the information they need. Whereas with take chat to you, for example, people are having long old conversations they're asking questions with three or four different aspects to it that kind of wouldn't work with a traditional search engine so you don't need that Google food anymore the LLM is sitting there in the middle and almost acting like a intent decoder. So how do we get from traditional search terms to those conversations there's a couple of things we do to take us from traditional search to those kind of prompts that people are using the first is we have a little personalization process. Why this is important is if you are in chat GPT and you said hey i'm a vegan give me a recipe for tonight and then six hours later you said i'm going to start running recommend me some running shoes. It would say ah because you're a vegan here are some cruelty free brands and running shoes that don't have leather in so it's impacted the type of result you're getting so the first thing we do is we use the chat GPT API. To actually pass over a description of our ideal customer profile along with the traditional keywords so we might say in an in a prompt to chat GPT if I was a middle aged guy who's just starting running and I'm health conscious and vegan and I was looking for this information and then I would pass over the traditional keywords that I'm interested in you know like running shoes that kind of thing. What kind of things might I ask chat GPT now I've always previously told people never use lm to do keyword research this is not keyword research in terms of we're not in this isn't we're trying to find out what people are typing into Google. This is perfectly aligned with the training data of lm's because they have hoovered up all of the hypers specific forums the reddit threads or someone says hey i'm a 42 year old guy i'm just starting running you know i'm posting on the vegan subreddit what trainers you know might I get so it's perfect to give you the type of queries those customer profiles might be asking for and you can ask several. The other dimension we want is what are the next most likely questions that person is going to to ask and that's actually then when we bring in the also asked API which is the Google people also asked data so that's going to give us whatever question we provide the nearest intent proximity questions and it's so fascinating looking at these because people are so predictable. So I use this example before with running shoes right and then you have the question about how much does a good pair of running shoes cost and then immediately the next post likely question is something along the lines of how much faster will good running shoes make me because people have seen okay well there's a 50 euro pair and a 200 euro pair how do I justify the 200 euro pair how much time is it going to take off my 5k. So actually from a list of say you had just 100 traditional keywords once you push them through 2 or 3 different ICPs once you've asked to chat to you what kind of queries they're going to generate you may get 3 or 4,000 prompts from that and it's those prompts then we run through the the grounding API or the grounding predictions we can cut them right back. So then we end up with these are sets of questions that are very likely to come up in conversations we know that for sure because they come from data they're very likely to be phrased in this way because it mimics how people talk in forums and how they have real conversations with other people which is how people interact with those chat bots with those AI search platform and then we know which ones are likely to be grounded and of course from the grounding API we can then tie each of those to. This is the list of key phrases that the AI search is going to do in the background which will normally take place on either Google or or being depending which which search engine you're using. It's interesting that you talk about Google Gemini grounding API but you also talk about chat GPT there as well are both AI search engines quite similar in terms of which types of queries they would require more grounding for. Yes they are yeah so I think this is got to do with the initial kind of the language graph and how much consistency there is in the answer they can generate so what I found particularly interesting is one of the tricky things with traditional keyword research was the same search term could mean two different things to two different people. And conversely you might have ten people wanting the same thing but all typing in different searches yeah the thing that I found interesting looking at the. Search as these models are doing is there's a remarkable consistency to how they perform their web searches because they have a fairly static OK this is the information I need this is the search I need to trigger to do that so it's more like. You're dealing with one entity almost for those searches and that gives you then a nice target to aim for because the outcome of this is essentially you have your key phrases for traditional search pages that you need to influence to appear in the AI search because that's the final step. And it does vary a little bit from what you might call traditional SEO because traditional SEO is this is my website and I would like it to rank high for these queries. Whereas the goal of this is if you've identified say three or four key phrases that we know AI is searching for the goal is simply to have your information present across as many of those results as possible. So if we continue with the shoe example when you're trying to sell your brand of shoes it might be OK the first two review websites the fifth one is a blog we can probably be present in all of those if we send them a sample and talk to them and do some PR with them. Because the AI of course is normally summarizing maybe the first 10 20 results is quickly checking them all out unlike the human behavior of you do the search and then a lot of the activities in those first few results vital that you're there. I've also heard you talking about building a pool synthetic prompts from traditional searches what does this exactly involve and how reliable these searches compared with actual data. So this synthetic prompt process was essentially what I was describing around taking traditional search and adding this layer of personalization and then expanding the conversation in terms of how reliable is the data. I would say it's not reliable in terms of this is precisely what someone is is typing in because nobody can get that data the only way to get that data would be to do you know very invasive click stream monitoring on everyone which just isn't going to happen with the way privacy laws are going. What I will say though is it is very accurate in terms of what we want to know which is what are the actual searches being triggered in the background because this is what I was saying about if you have 10 questions that are worded slightly differently but they mean the same thing. They will all generate the same searches in the background and that's what we're interested in so while I don't know precisely the word order that some individuals might use it doesn't matter because the Google food thing is happening through the LLM and we have seen a remarkable consistency with them saying OK, they're trying to find out this information to a sensible thing for me to a web search for will be these things and that's why it's such a useful powerful technique because that's the thing you need to impact to actually get seen in the end answer. I'm not sure of grounding or Google food gets the word of today but well, I'm pretty impressive. Mark was the key takeaway from the tip you showed today. The key takeaway would be that even if you're doing traditional SEO now if you're thinking about AI search it's a multi site approach so it's not just about getting your site to rank it's about getting your service your brand your products talked about in all of those results are coming up and make sure you've got good sentiment that describes what you're doing because that's what the AI is going to be filtering for. Mark Williams Cook is director at candor and founder of also asked find out more over at with candor.co.uk. Mark thanks so much for being part of SEO in 2026. Thank you for having me. I've been your host David Bain. Get your copy of SEO in 2026 the book over at SEO in 2026.com. [BLANK_AUDIO]