How Do LLMs Work?

Large Language Models (LLMs) like ChatGPT, which predict the next most likely word based on vast datasets, have become synonymous with AI but represent just one aspect of the broader AI landscape. While they excel at generating general text by identifying patterns and associations from extensive training data, they lack true understanding and face significant limitations, such as restricted context windows and difficulty focusing on the most relevant details. These limitations become apparent when dealing with specific, nuanced queries, particularly in proprietary data environments. To enhance LLMs' capabilities, two primary approaches are used: Retrieval-Augmented Generation (RAG), which supplements prompts with the most relevant data from external sources, and fine-tuning, which involves additional training on specific datasets to improve the model's performance. Despite their impressive capabilities, LLMs remain probabilistic models that require supplementary techniques to address their inherent limitations in handling complex, context-specific information.

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