Large Language Model Architecture Explained

LLM architecture is built on the transformer with self-attention mechanisms that allow every word to attend to every other word weighted by relevance. Training occurs in two phases: pre-training on massive corpora to predict next words, then fine-tuning with curated datasets plus RLHF alignment.

Model size correlates with capability but also cost and latency — techniques like quantization, distillation, and mixture-of-experts aim to deliver larger-model performance at smaller cost. Context window size determines how much text can be processed at once, with modern models supporting 100,000+ tokens. Efficient context management through summarization and retrieval augmentation is a critical design consideration.