Large Language Model: Definition and Examples

A Large Language Model is a type of artificial neural network designed to process, understand, and produce natural language text. These models are called 'large' because of their enormous number of parameters—often tens or even hundreds of billions—and the massive amount of textual data on which they are trained.

The functioning of an LLM relies on an architecture called Transformer, introduced in 2017 by Google researchers. This architecture allows the model to analyze relationships between all words in a text simultaneously, thanks to an attention mechanism. During training, the model learns to predict the next word in a sequence, enabling it to acquire a deep statistical understanding of language, grammar, facts, and even some forms of reasoning.

Among the most well-known LLMs are OpenAI's GPT-4, Anthropic's Claude, Google's Gemini, and Meta's LLaMA. These models can perform a wide variety of tasks: writing, translation, summarization, code analysis, question answering, and much more, often without being explicitly programmed for each task.

The emergence of LLMs has profoundly transformed the field of prompt engineering. Unlike traditional software where you write code, interacting with an LLM involves formulating instructions in natural language—the prompts. The quality of the response directly depends on the clarity, precision, and structure of the prompt, making prompt engineering an essential skill to fully exploit the potential of these models.