LLM Fundamentals
definition
Large language models generate text through next-token prediction, using transformer architectures with self-attention mechanisms to process and produce sequences. Understanding how scale, training data, and architecture choices affect model capabilities is essential for building effective agents — these fundamentals explain why models can follow instructions, use tools, and reason through complex problems.
Large language models generate text through next-token prediction, using transformer architectures with self-attention mechanisms to process and produce sequences. Understanding how scale, training data, and architecture choices affect model capabilities is essential for building effective agents — these fundamentals explain why models can follow instructions, use tools, and reason through complex problems. The relationship between pretraining data, fine-tuning, and reinforcement learning from human feedback (RLHF) determines a model's personality and capabilities, which directly impacts how agents behave in production. This concept connects to token economics for cost modeling, model selection for choosing the right model per task, and context engineering for understanding what information the model actually processes.