A glossary of key terms that are used in today’s generative engine optimization (GEO) profession.
GEO Terms – A to Z Order
AI Leveraging
Using data and insights from how AI systems work (e.g., source information, respond to prompts) to inform a brand’s content creation and optimization strategies for AI models.
AI Optimization
The process of structuring content in a way that AI can easily understand, interpret, and use – increasing a brand’s chances of being displayed in AI model responses. This would involve the following:
- Using clear and concise language
- Building a strong online brand presence
- Citing reliable sources and expert quotes
- Monitoring and adapting to evolving behavior by AI systems
- Generating content that answers specific user queries (“answer-first” optimization)
- Addressing technical SEO (e.g., structured data, site speed, and mobile responsiveness)
- Enhancing content readability and AI parsing (e.g., heading hierarchy, listicles, and tables)
- Test the AI models – ask them questions typically asked by your customers – does your brand appear in answers?
Algorithmic Transparency
The degree to which the workings of a generative AI model are understandable and explainable. In GEO, this relates to understanding how a model generates content and the factors that influence its output.
ChatGPT
An AI chatbot developed by OpenAI that uses large language model (LLM) technology. ChatGPT scrapes existing datasets to train its LLM on what types of output to provide in response to a user’s query.
Claude
A family of AI assistants and large language models (LLMs) developed by Anthropic, which was founded by former members of OpenAI in 2021. The company’s mission is focused on developing AI systems which prioritize ethical considerations.
Content Atomization
The process of breaking down a large piece of content into smaller, standalone pieces that can be repurposed across various platforms. This is a key strategy in GEO to maximize content reach and efficiency.
Contextual Relevance
Contextual Relevance: How well the generated content aligns with the user’s query and the broader context of the search. High contextual relevance is crucial for GEO success.
Data Poisoning
A malicious attack where deceptive or misleading data is fed into an AI model’s training dataset to corrupt its output. This can negatively impact the quality and accuracy of generative content.
Feature Engineering
The process of selecting and transforming raw data into features that can improve a machine learning model’s performance. In GEO, this can involve optimizing input prompts or data to get better generative results.
Gemini
An AI chatbot developed by Google that functions similarly to ChatGPT but has the added advantage of pulling real-time information from Google’s other products such as Search, Maps and Google Earth. As a result, Gemini’s output is oftentimes more current than that of other AI models.
Generative Adversarial Networks (GANs)
A class of machine learning frameworks where two neural networks—a “generator” and a “discriminator”—compete against each other. GANs are often used to create realistic images, videos, or other media.
Generative Engine Optimization (GEO)
GEO is the practice of optimizing a brand and its content for maximum visibility in today’s AI-driven search engines like ChatGPT, LLM powered search engines like Perplexity, and Google’s AI Overviews – instead of ranking web pages, GEO positions brands as a direct source of answers that can be understood and extracted by AI.
This goes beyond traditional SEO – the aim of GEO is to ensure your content is deemed trustworthy enough to be cited by the variety of today’s AI systems, when they provide their users with specific answers or information in response to conversational queries. Having a strong background in SEO will lay a good foundation for understanding and incorporating GEO into a brand’s online marketing operations.
The goal should be to have your brand (and its content) prominently featured in AI responses as a valuable source of information on a given subject matter!
Hallucination
A phenomenon in which a generative AI model produces false or nonsensical information. In GEO, mitigating hallucinations is a priority to ensure the content is accurate and trustworthy.
In-context Learning
The ability of a large language model (LLM) to learn from a few examples provided in the input prompt, without requiring an update to its underlying parameters. This is a powerful technique for fine-tuning generative output.
Intent-based Optimization
A GEO strategy that focuses on understanding and addressing the user’s underlying intent behind their search query. This goes beyond simple keywords to provide more comprehensive and satisfying answers.
Large Language Models (LLMs)
A type of AI model trained on a massive amount of text data to understand and generate human-like language. LLMs are the core technology behind many generative AI applications used in GEO.
Model Drift
A change in the performance of an AI model over time due to a shift in the underlying data or user behavior. Monitoring for model drift is important to ensure the continued effectiveness of GEO strategies.
Natural Language Generation (NLG)
A subfield of AI that focuses on creating human-readable text from structured data. NLG is a fundamental component of generative AI for content creation.
Perplexity
An AI-powered search engine that provides answers to user queries. It is different than traditional search engines because it offers more of a conversational approach, rather than just a list of hyperlinks.
Prompt Engineering
The art and science of crafting effective prompts to guide a generative AI model to produce the desired output. This is a fundamental skill in GEO.
Reinforcement Learning from Human Feedback (RLHF)
A technique for training AI models using human feedback to better align their outputs with human values and preferences. RLHF is used to make models more helpful and less prone to generating harmful or biased content.
Semantic Search
A type of search that goes beyond keyword matching to understand the meaning and context of a user’s query. Generative AI is a key enabler of semantic search, as it can understand and synthesize information to provide more relevant results.
Topical Authority
A metric used by search engines to gauge a website’s expertise on a particular subject area. It is earned by creating comprehensive, high-quality content that covers a topic in its entirety, rather than just focusing on a few select keywords.
This is typically accomplished by creating a main pillar page discussing a central topic, which is then linked to supporting cluster pages addressing relevant subtopics.
Exemplar pillar page:
- Golden retriever puppies
Exemplar cluster pages:
- How to groom a golden retriever
- How to train a golden retriever puppy
- What to feed golden retriever puppies
- Best methods for exercising a golden retriever puppy
- Socializing your golden retriever with other dog breeds