AI Definitions for SEO & LLMO

Chaos (--chaos, -c): A parameter in Midjourney that controlls the temperature (or variability) of the generate image. Can range from 0 to 100. Higher values can produce more unexpected results.

CoT (Chain-of-Thought): A prompting technique that improves LLM reasoning by prompting the model to explain its thought process step-by-step.

EAGLE-2 (Extrapolation Algorithm for Greater Language-model Efficiency 2): An approach to use dynamic draft trees to accelerate LLM inference by adjusting token predictions based on confidence scores and context.

Foundational Model: A large AI model trained on extensive data, serving as a foundation for specific tasks and applications.

GAIO (Generative AI Optimization): Strategies to purposefully guide an LLM’s responses to enhance the visibility of a website or brand.

General Intelligence: Broad knowledge of the world combined with capabilities in common sense, planning, and reasoning.

GEO (Generative Engine Optimization): Measures to influence an LLM's responses, primarily to improve the positioning of a website or brand.

GVL (Generative Value Learning): A framework that uses vision-language models to estimate task progress by interpreting visual sequences. Can significantly reduce the need for pre-training!

Inference: The process by which a large language model generates outputs (tokens) in response to a prompt.

KG (Knowledge Graph): A structured representation of information that shows relationships between entities, often used to improve the precision and relevance of search results.

LLM (Large Language Model): A large language model trained on extensive datasets to understand and generate natural language.

LLMO (Large Language Model Optimization): The process of implementing measures to influence an LLM’s responses, usually to better position a website or brand.

Narrow Intelligence: Specialized knowledge and skills focused on performing specific tasks, like text translation or image recognition.

NER (Named Entity Recognition): A technique that identifies specific entities (like names or locations) in unstructured text.

NLG (Natural Language Generation): The generation of natural language by AI to create understandable text.

NLP (Natural Language Processing): The processing of natural language to analyze and interpret human communication.

NLU (Natural Language Understanding): An AI’s ability to grasp the meaning and intent behind human language.

NTF (Nonnegative Tensor Factorization): A mathematical technique to decompose a tensor into a set of non-negative component tensors. NTF helps in extracting underlying patterns, especially in multi-dimensional data such as time-series or multi-modal datasets. Use cases include image analysis and topic modeling.

RAG (Retrieval-Augmented Generation): A technique in which a language model retrieves external information to provide more precise and contextually relevant answers.

RIG (Retrieval-Informed Generation): Similar to RAG, but with dynamic and continuous retrieval of information during response generation.

RIG (Retrieval-Integrated Generation): Similar to RAG, but instead of treating retrieval and generation as two sequential steps, the response system can dynamically retrieve data multiple times during answer generation.

VS (Vector Store): A database that contains a vectorized representation of unstructured data, like text.