Key Terminology: AIO, AEO, GEO, and LLM
To navigate the new world of AI search, it's essential to understand the new terminology that defines the different facets of optimization.
AIO: The Umbrella Term
AI Optimization (AIO) is a broad term that refers to leveraging artificial intelligence to enhance performance and efficiency.
It can be broken down into three main categories:
- AI model optimization: Enhancing AI systems themselves to be faster, more accurate, and more efficient.
- AI process optimization: Improving and automating various business workflows and operations.
- AI-driven optimization: Using AI to improve specific strategies, such as enhancing website experiences or content discoverability in search.
It is within this third category that the specialized disciplines of AEO and GEO fit.
AEO: Answer Engine Optimization
Answer Engine Optimization (AEO) is a smarter, sharper way to get your content in front of people (and their AI buddies) by directly answering their questions.
If you're thinking, "Wait, isn't that just SEO with a different outfit?" - well, not quite. SEO gets you on the map. AEO gets you picked by the tour guide—the guide being Siri, ChatGPT, Alexa, Google Assistant, or any AI engine parsing the web to answer a user's question. The primary goal of AEO is to be featured in AI-generated answers or voice responses, focusing on directly answering specific user questions.
GEO: Generative Engine Optimization
Generative engine optimization (GEO) is the process of optimizing content on web pages with the goal of ensuring it is properly displayed in AI-driven search engines such as ChatGPT, Perplexity, Gemini, Copilot, and others.
The purpose of GEO is to improve the visibility of a website within popular large language models (LLMs), enhance brand awareness online, increase organic traffic, and improve user experience and satisfaction across AI-driven platforms.
The Role of Large Language Models (LLMs)
A large language model, or LLM, is a kind of artificial intelligence that's designed to understand, generate, and interact with language.
Think of it as an AI "brain" trained on a massive amount of text and data from the internet, books, and other sources. This extensive training allows it to recognize patterns, context, and nuances in language, so it can answer questions, write essays, translate languages, summarize documents, and engage in full-on conversations. Popular examples of systems that use LLMs include ChatGPT, Google's Gemini, and Claude. LLM optimization is a highly technical discipline that focuses on enhancing the core engine of the large language model itself to make it faster, more accurate, and more efficient.