The Right Way to Use Multiple Embedding Models

When using multiple embedding models in a machine learning pipeline, it's crucial not to compare their representations (like through Euclidean distance or cosine similarity) because they exist in different spaces, meaning their axes aren't aligned. Instead, concatenation is a more effective approach and works even if the embeddings have unequal dimensions.