Large Language Models

Large Language Models are neural networks with billions of parameters trained on vast text corpora to predict the next token, enabling them to generate coherent paragraphs, translate languages, write code, and answer questions. Built on the Transformer architecture, they learn word meanings and context through self-attention layers and masked-language objectives. Fine-tuning, instruction tuning, and reinforcement learning from human feedback (RLHF) align these models—such as GPT-4, Gemini, and Llama 3—with specific tasks or safety guidelines. In production, LLMs power chatbots, content summarizers, and Retrieval-Augmented Generation (RAG) systems that ground responses in private data. Key metrics include perplexity for fluency, factuality scores for accuracy, and token-level latency for user experience. Challenges involve hallucinations, bias, and high inference cost, mitigated by prompt engineering, guardrails, and model distillation. By converting statistical text patterns into versatile reasoning engines, Large Language Models redefine how software understands and generates human language.