What is the role of vector embeddings in a large language model (LLM)?

Vector embeddings transform text (words, phrases, or documents) into numerical representations (vectors) in a high-dimensional space. This process allows LLMs to understand the semantic meaning and relationships between different pieces of text. By converting text into vectors, mathematical operations like calculating distances or angles between these vectors become possible. Similar texts will have vectors that are close together in this space, enabling the LLM to identify related concepts, perform semantic search, or generate coherent responses. Splitting text into smaller pieces (tokenization) is a preprocessing step, not the role of embeddings themselves. Grouping characters into tokens is also part of tokenization. Counting word occurrences is a basic statistical analysis, not the core function of vector embeddings.