Embedding, in the context of Generative AI, refers to the mathematical representation of complex data types, such as words, sentences, or even entire documents, in a high-dimensional space. These representations, often in the form of vectors, capture the semantic or contextual meaning of the data. Embeddings are a crucial part of many Machine Learning models, as they allow these models to understand and process data in a more human-like way. They are particularly useful in Natural Language Processing, recommendation systems, and other areas where understanding the relationships between different pieces of data is important.
See also Vector Store and Vector Search.
The LLM Knowledge Base is a collection of bite-sized explanations for commonly used terms and abbreviations related to Large Language Models and Generative AI.
It's an educational resource that helps you stay up-to-date with the latest developments in AI research and its applications.