The Must-Know Topics for an LLM Engineer

Large Language Models convert text into numbers for processing through a pipeline of tokenization, embeddings, and positional encoding. The core of these models is the transformer architecture, which relies on the attention mechanism to weigh the importance of different tokens when processing a sequence. Different model architectures like encoder-only, decoder-only, and encoder-decoder are used for specific tasks such as classification, text generation, or translation, respectively. Ultimately, these models are trained to predict the next token in a sequence, generating a probability distribution over the vocabulary that is then used to decode the final output text.