Large Language Model

A large language model (LLM) generates humanlike sentences by learning from extensive text and predicting subsequent tokens probabilistically. Model families such as GPT, Claude, Gemini and Llama differ in their training data, parameter scale and reinforcement-learning approaches, producing different answer styles and levels of accuracy for the same question. Models have a knowledge cutoff because pretraining covers information collected only up to a particular time. Questions requiring current information therefore benefit from retrieval-augmented generation (RAG) combined with live web retrieval.

Business applications and impact#

Provides the core intelligence behind next-generation search engines and intelligent software.

Implementation guidance and considerations#

Understand token processing and the limits of pretrained knowledge when designing optimization strategies.