Ep 4.2: LLM, GEO, AEO—What Do All These AI Names Mean?

We remember back in the day when SEO and PPC were just about the only abbreviations we had to worry about, but then came along AI, and boy oh boy, have things gotten just a little bit more alphabetty.

In this episode, Genny’s going to run through the list of the most common AI-related terms that marketers most likely encounter on their mission to maximise their visibility on AI-powered search. 

In case you want spoilers, here’s the list:

AI (Artificial Intelligence)

• Any technology that simulates human intelligence in machines that allows them to perform tasks like recognise patterns, make predictions, and understand language.

GenAI (Generative AI)

• A type of AI that can generate new content, (i.e., text, images, music, or videos) based on its training data. Examples include ChatGPT, MidJourney, and Deepfakes.

GEO (Generative Engine Optimization)

• This is the process of optimising web content to maximise your brand visibility in generative AI systems (like ChatGPT) by ensuring your content gets selected as answers in AI-generated responses.

AEO (Answer Engine Optimization)

• Synonymous with GEO, AEO focuses on optimizing content so that it appears in AI-generated answers, particularly for Answer Engines.

AIO (AI Overviews)

• AIO refers to AI Overviews, those helpful content summaries at the top of Google’s search results, which include a short overview with relevant links.

LLM (Large Language Model)

• A type of AI that’s trained on vast amounts of text data that can process and generate human-like language. LLMs like GPT-3 can answer questions, generate text, and more, but they are based on fixed training data (they don’t learn in real time).

NLP (Natural Language Processing)

• This is a subset of AI that’s focused on helping machines understand, interpret, and generate human language, e.g., speech recognition, text analysis, and machine translation.

ML (Machine Learning)

• Another subset of AI where machines learn from data to recognize patterns and make predictions. 

DL (Deep Learning)

• A part of machine learning that uses neural networks with multiple layers (hence the term "deep") to process and understand complex data.

AGI (Artificial General Intelligence)

• A type of AI that can learn and apply knowledge across various domains, just like humans. Unlike narrow AI, which excels at specific tasks (e.g., chatbots), AGI can perform a wide variety of tasks with human-like reasoning.

Hallucinations (AI Hallucinations)

• In this context, hallucinations refer to instances where the model generates incorrect or totally made up information that appears convincing. 

GPT (Generative Pre-trained Transformer)

• A type of LLM (like ChatGPT), that uses transformer architecture to generate human-like text based on large-scale pretraining. GPT can generate, summarise, or answer questions based on data it has been trained on.

Remember, we’re at the very start of our AI journey, so don't let information overwhelm get in the way of AI adoption!

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Transcript

Y’ever noticed that the more sophisticated technology gets, the lazier we are at naming it? Back in the day when our ancestors invented shit, they’d come up with the dopest of words like the crowbar, the X-ray, the daguerreotype, and hell—even the spinning jenny for crying out loud.

Now it’s like we’ve literally made a big batch of alphabet soup and are just randomly spooning letters out as soon as something new pops up on the technological horizon. Where’s the creativity I ask you?

As an S-E-O agency, we know our way around our abbreviations, but what about all the new stuff that’s being created around A-I, everyone’s favourite future threat to humanity? I’m serious, there are so many new AI-related technologies circling the drain that it’s hard to keep up. You’ve got your LLM, your AEO, AIO, your ML, AGI, your NLP, GEO, and then stuff like GenAI and Weak AI and Strong AI and agentic AI—my god, what I wouldn’t give for spinning jenny right about now.

If you’re up to your bib in AI’s alphabet soup, stick around because all I’m going to do for this episode is run through the list of the emerging AI-related technology and explain what all of it means because unless you subscribe to Artificial Intelligence Weekly or AI Digest (two publications I just made up that might actually exist), you’ve likely already been bamboozled and hoodwinked by these abbreviations and terms but have been too shy to ask. Stick with me kid. I gotchu.

First on our list: AI. Artificial intelligence, the technology that’s putting people, profit-seekers, and policymakers in a real tizzy. So what’s AI all about? How far back should I go? If you’re technologically intelligent enough to be listening to this podcast episode right now, then you’re probably well aware at least to some degree what artificial intelligence is, right? I don’t need to tell you that AI is, from its own lips by the way, the simulation of human intelligence processes by machines, particularly computer systems. 

If you’re thinking of C-3PO or R2D2 or even Data from Next Generation, then you know what AI is. Although obviously, AI has so many more applications than being a little helper.

What makes AI so fascinating and kind of terrifying if you’ve ever read any science fiction written in the last two hundred years, is its sheer capability of mimicking cognitive functions such as the ability to recognize patterns, make predictions, or even understand natural language—and that’s not even getting into its potential for bias, discrimination, sexism, all the gross stuff. Okay, I don’t want to spend too much time on AI because it already feels a little moooot, so let’s dive into another abbreviation.

I was going to go in alphabetical order, but I think I need to start broad and work my way in.

So first on the list is GenAI, which is shorthand for generative artificial intelligence, meaning a type of AI that has the ability to create, or generate, something, and by something I mean text, images, music, videos, and even code—you know, all the creative stuff that sets humans apart from cockroaches. Of course, it doesn’t yet have the capacity to pull something out of thin air. The content it creates is based on its training data. GenAI is characterised by its creativity, adaptability, and trainability, if that’s a word. And examples of GenAI include, of course, the world-famous ChatGPT, also Midjourney, chatbots, deepfake videos, you know. So that’s GenAI.

Next, we’ll mosey on over to GEO, which, as you might guess, stands for generative engine optimisation. Ooh another term starting with generative, what could it mean? If you’re coming from the marketing world, then this will likely sound more familiar to you, but even so, GEO is the process that focuses on optimising your web content in a way that maximises the effectiveness of generative AI systems. What I mean by that is GEO is a set of techniques used to increase the chances that your content or brand will be used to answer queries asked in GenAI systems. 

So for example, I type in the question, ‘What are the best microphones to create ASMR videos?’ into ChatGPT and it gives me a list of brands, a breakdown of why they’re good, and what features I should look for in a microphone. Now, imagine if that was your brand, plucked out of the crowd and held up as a shining example of one of the best brands on the market. You too can experience this level of visibility with a little GEO.  

And on that note, let’s move on to GEO’s synonym, which is AEO. Old MacDonald had a farm, A E O E O. Sorry, I just had to. Okay I didn’t have to, but I wanted to. 

AEO. What does that stand for? It stands for Answer Engine Optimisation. And what is answer engine optimisation? Why, it’s the same thing as GEO, of course. The more we work with clients, the more these new abbreviations get tossed around, and the more confusing things get, but as long as you remember that GEO and AEO are effectively the same thing, which is optimising your content so that your brand appears in answers to AI search queries, then everything will be hunky-dory.

Next, we’ve got AIO. We’re sticking with open-mouthed abbreviations. Okay, this one is actually quite interesting because we’ve even had internal debates about this. What does AIO mean to you? Think about it for a second. A-I-O….it must mean AI optimisation, right? Well you would be right and wrong. I cannot sit here and tell you that AIO doesn’t stand for AI optimisation because obviously it does. Buuuuuuuuuut, in the context of, I don’t what, in the context of marketing? In the context of GEO, I guess, AIO doesn’t stand for AI optimisation (although it obviously does), it stands for AI Overviews, Google AI Overviews to be exact, you know the thing at the top of Google SERPs that gives you a summary with links. So that’s AIO.

Coming up next, we’ve got LLM, so stick around folks.

LLM is probably relevant to most people listening to this episode, and something that we’ve noticed confuses a few people. LLM simply stands for large language model, and is a branch of artificial intelligence that processes and generates human-sounding language. These bad boys are called ‘large’ because they’re trained on big fat mamajama sets of training data. This training data is typically not continuous, which is to say that it learns from large amounts of data you feed it for a limited amount of time, and that’s it. Some do have web search capabilities, but these are typically add-ons, and unless you ask it to include recent web searches in your query, it’s going to answer based on its training data, and not on web pages.  So, for example, if you wanted to ask a lower model of ChatGPT a question about something that happened in 2025, it just simply wouldn’t know the answer because its training cut off date ended before that year. Why is this important for marketers? Because if you’ve optimised your site for AI and you’re not seeing your new content pop up in an AI-generated response, that’s probably because the LLM is referencing its training data. Please bear that in mind before you angrily shake your fist at your GEO agency. 

Next on the docket we got, NLP, which, if you’re prone to using crystals and consulting the astrological charts, likely stands for neurolinguistic programming to you, but in this context, NLP stands for natural language processing. NLP is a kind of AI that helps machines or computers understand, interpret, and generate meaningful and useful “human” language. With NLP, computers can take large amounts of data (like text or speech) and make it easy for humans and machines to talk to each other, just like every single robot in movies. But also, more realistically, Siri on your phone. Besides the obvious speech recognition, some of NLP’s main functions include text and sentiment analysis, translation, named entity recognition, or NER—oh there’s another abbreviation for you—which is the computer’s ability to recognize and classify things for what they are. For example, knowing that Little Rock is a city in Arkansas and not a small stone; or that Microsoft is a company, and that February 29th is a date, even a special date; NLP can also understand parts of speech, answer questions, summarise text and even convert text to speech or vice versa.

So that’s NLP.

The next stop on our journey is ML, or machine learning. A machine that learns? What is this sorcery? It’s another type of artificial intelligence that allows computers to take the data they’re given, recognise patterns within the data, and use that to make predictions and decisions without being specifically told to. Obviously I’m glossing over quite a lot, so if you’re interested in learning more about ML, why not just ask your trusty AI friend to explain it in more detail?

Machine learning is seemingly embedded in pretty much every app, every social media platform, and just everywhere. From personalised product and video recommendations to fraud detection to fitness trackers to self-driving cars and literally every kind of application in between, ML is pretty ubiquitous these days. 

So, next up we have DL, or deep learning, which you may or may not have come across in your exploration of AI. DL is a subset of ML that uses neural networks, which are effectively the equivalent of robot neurons, to understand deeply complex information. Its capacity is modelled off of the human brain, so it can do things like detect objects, recognize faces, help self-driving cars understand their environment, and even diagnose diseases.

I think we’re coming to the end of our abbreviations, except maybe I’ll add here AGI, which stands for Artificial General Intelligence, which is also known as ‘strong AI’ or ‘human-level AI’ and refers to a type of AI that learns and applies its knowledge across multiple applications as opposed to narrow or weak AI, which is designed to be a one-trick pony, like chatbots, self-driving cars, and spotify playlists. 

Okay, I’m almost done being your slightly hyper human dictionary, but there are a few more terms you should probably know while we’re at it.

Like what the hell is this talk about hallucinations? Have you ever heard this term before? It’s actually important to keep this in mind when using AI in your everyday life which is the fact that sometimes AI’s output (or answer) contains false information that’s presented so convincingly it’s as if the AI took a bunch of LSD and is just making stuff up with full confidence. 

Oh, and I guess maybe GPT, yeah you know me. You down with GPT? Oh god, I need to stop soon. GPT, or ChatGPT to its friends, stands for generative pre-trained transformer, which is, using its own words, a deep learning model for natural language processing that uses large-scale pretraining to understand and generate human-like text. 

So there you have it. Trust me children, if I went through the entire catalogue of all the new inventions, innovations, and branches being birthed by AI, we would be here all day and my throat would eventually close up. Bear in mind, that I assume that the majority of people listening are coming from a marketing angle, so this list was intended to help clarify some of the terms you’ve encountered and tripped over. 

I hope this has helped. The show notes will be there for you. I will clean it up so you can print it out and stick it on your fridge or tape it to your monitor at work. Hey you don’t have to thank me. 

That’s all for now. Remember kids, we’re all just origami boats floating down the crazy AI river. The possibilities are literally endless when it comes to AI and marketing, so I have no doubt that we will be covering this topic in the months to come. If you have any questions about this episode feel free to message us at team@geekytech.co.uk and check us out on social media @geekytechgeeks.