Behind Generative AI such as Gemini, ChatGPT, or Microsoft’s Copilot, there is a machine categorized as a Large Language Model (LLM). This means that the machine has built a model pre-trained using datasets containing billions of words based on a wide range of texts such as documents, books, articles, or websites. This vast wealth of information allows the LLM to determine how statistically likely it is that two words are used together in the same context and in a particular order. This is similar to how the autocomplete feature on your phone predicts the next word when you are typing a message, except that these LLMs take that capability to the next level due to their advanced design and sheer amount of data behind their training.

Despite this is a technology that is still in its early steps, the performance and capabilities of the most powerful LLMs have the potential to improve with each new version. OpenAI’s GPT 4o will make room to GPT 5 and even Meta’s powerful open-source model Llama 3.1, trained with 405 billions of parameters, eventually will be replaced by a more advanced version.

These models can benefit the users directly through user friendly interfaces such as ChatGPT, but the biggest potential is on integrating them into AI solutions where, for a specific use case, a software can take advantage of the natural language capabilities of these models under the hood.

For a real-world case, let’s look at Amazon. When their customers write a complaint about product defects or delivery delays, a system based on a LLM analyzes hundreds of those complaints to identify patterns. If the model identifies an unusual spike of complaints related to a particular fulfillment center or a supplier, the system alerts Amazon’s procurement team, which can investigate the cause and take action with the supplier.