In AI, a token is a text unit the model reads. An embedding is a numeric vector that represents meaning for search and retrieval.
A token represents a fundamental unit of text or data processed by large language models (LLMs). Think of it as a word, part of a word, or punctuation mark that the AI breaks down for analysis. When you interact with an LLM, your input is tokenized, and the model generates.
A token is a basic unit of text or data processed by large language models (LLMs), crucial for understanding AI performance and cost.
Full PathTerms™ definitionAn embedding is a way to represent complex data, like words, images, or user preferences, as numerical vectors in a multi-dimensional space. This process transforms discrete or high-dimensional information into a continuous vector format that machine learning models can easily.
Embeddings represent complex data as numerical vectors, capturing relationships for AI processing and analysis.
Full PathTerms™ definitionPeople mix up Token and Embedding in exam questions and job posts. Knowing the difference helps you pick the right control, process, or architecture choice.
In AI, a token is a text unit the model reads. An embedding is a numeric vector that represents meaning for search and retrieval.
