Transformers are the backbone of modern Generative AI models. They process and generate data such as text, code, or images by using a mechanism called self-attention, which allows the model to weigh the importance of each word (or token) in a sequence relative to the others. Unlike traditional models that process data sequentially, transformers look at the entire input at once, making them highly efficient and effective for capturing long-range dependencies and context.

In Gen AI, this ability is crucial. For example, in language models like GPT, transformers analyze large volumes of text to learn patterns, grammar, facts, and even reasoning skills. The model uses layers of encoders and decoders to understand and then generate coherent, human-like content. With massive training datasets and billions of parameters, transformers become adept at producing creative outputs, answering questions, and even generating code or images.

Transformers have revolutionized AI due to their scalability and adaptability, making them the foundation for most state-of-the-art Generative AI applications today.

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