How Large Language Models Work: Explained Simply

This article provides a simple explanation of how large language models (LLMs) work, including the transformer architecture, pre-training, fine-tuning, and retrieval-augmented generation (RAG) systems.

Details

Large language models (LLMs) like GPT, Gemini, and Kimi are built on the transformer architecture, which uses attention mechanisms to determine the most relevant parts of the input when generating each output token. These models are first pre-trained on massive amounts of text data from the internet, lectures, articles, and other sources. During pre-training, the text is split into tokens, which are then converted into numerical embeddings. The model analyzes these embeddings to learn statistical patterns and relationships between words and phrases. After pre-training, the models are further fine-tuned using Reinforcement Learning from Human Feedback (RLHF), where human evaluators rank or compare the model's outputs, and the model is optimized to provide more helpful and safe responses. Some advanced LLM systems also incorporate retrieval-augmented generation (RAG), where the model can access and incorporate relevant information from external documents to enhance its responses. This combination of large-scale training, attention mechanisms, and access to external knowledge is what makes modern LLMs so powerful, even though they are still fundamentally probabilistic text generators without true understanding.