Large Language Models (LLMs) represent a major breakthrough in artificial intelligence. These models are trained on billions of text documents — books, articles, web pages — to acquire a deep statistical understanding of natural language. Unlike a traditional database, an LLM does not store information word for word: it learns linguistic patterns that allow it to generate coherent, contextually relevant text.
In the legal field, LLMs already power a wide range of tools: case law research, drafting legal documents, summarising decisions, contract analysis. Law firms that adopt them report time savings of up to 50% on repetitive tasks. That said, an LLM is not a lawyer: it generates statistically probable text, not necessarily text that is legally accurate.
The challenge for legal professionals is to understand the strengths and limits of these models. Combining an LLM with techniques such as RAG (Retrieval Augmented Generation) and rigorous prompt engineering makes it possible to obtain reliable, verifiable results, grounded in real legal sources.