How LLMs Turn Your Prompt Into a Response: The Key Steps Explained

Large Language Models (LLMs) process user input through a multi-step pipeline rather than retrieving stored answers from a database. Text is first broken into smaller units called tokens by a tokenizer, and each token is assigned a numerical ID for the model to process. These token IDs are then converted into high-dimensional vectors known as embeddings, which capture richer semantic meaning learned during training. A mechanism called self-attention, central to the Transformer architecture, allows each token to incorporate context from surrounding tokens, enabling the model to resolve references and relationships within a sentence. The model ultimately predicts the most likely next token in sequence, repeating this process until a full response is generated.