LLM Fundamentals

Large language models (LLMs) generate text through next-token prediction, using transformer architectures with self-attention mechanisms to process and produce sequences of language. Understanding how scale, training data, and architecture choices affect model capabilities is essential for building effective agents, because 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 behavior and limitations, which directly shapes how agents perform in production.