How are embeddings used in generative AI models?

Embeddings are numerical representations of data that enable generative AI models to process and generate content. They convert discrete inputs like words, images, or sounds into continuous vectors—arrays of numbers—that capture semantic relationships. For example, in text generation, each word is mapped to a high-dimensional vector, allowing the model to understand similarities between words (e.g., “king” and “queen” are closer in vector space than “king” and “apple”). Similarly, in image generation, embeddings might represent patches of pixels or entire images as vectors. This numerical form is essential because neural networks operate on mathematical operations, and embeddings provide a way to translate raw data into a format these models can work with efficiently.

During training, embeddings are learned or fine-tuned to capture contextual and structural patterns. In transformer-based models like GPT, embeddings serve two roles: token embeddings represent individual words, while positional embeddings encode the order of tokens in a sequence. For instance, the word “bank” might have different embeddings depending on whether it appears in “river bank” or “bank account,” allowing the model to handle polysemy. In diffusion models for image generation, embeddings often represent latent features of images, which are iteratively refined during the denoising process. Pre-trained embeddings, such as Word2Vec or CLIP text encoders, are sometimes used to bootstrap training, providing a starting point that already encodes useful semantic relationships.

Embeddings improve generalization and enable cross-modal applications. For example, in Stable Diffusion, text prompts are converted into embeddings via CLIP, which guide the image generation process to align with the text description. In chatbots, embeddings help generate coherent responses by maintaining contextual consistency—each token’s embedding carries information about the conversation history. Embeddings also reduce computational complexity: instead of handling millions of unique tokens, models process dense vectors of fixed size. This efficiency allows generative models to scale to larger datasets and more complex tasks, such as translating medical reports into visual diagrams by mapping text embeddings to image embeddings. By abstracting data into a shared numerical space, embeddings bridge the gap between raw inputs and the generative capabilities of AI models.