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This article traces the path from RNNs to transformers, explaining why attention-based, non-recurrent architectures replaced older models. It then breaks down encoder vs decoder designs and shows how GPT’s decoder-only approach and tokenization power today’s large language models.
This article breaks down the core concepts behind LLMs—from next-token prediction training to tokens, vectors and attention layers—to show how they generate text. It also covers context windows, parameters and why model scale affects performance.