An embedding is a mapping from discrete objects, such as words, images, or entire documents, to points in a continuous vector space. Its primary purpose is to convert non-numeric data into a format that neural networks can process.
One of the earlier and most popular examples of word embedding is the Word2Vec approach.
Such embedding models can be used as pretrained models to generate embeddings for Machine Learning models, but LLMs produce their own embeddings as part of the input layer and they get updated during training. The advantage of optimizing the embeddings as part of the training is that they get optimized to the specific task and data at hand.