How AI Works Under the Hood – LLMs Explained with Code (nitayneeman.com)

A new article provides a deep dive into the workings of Large Language Models (LLMs) such as GPT, Claude, and Gemini, demystifying how text prompts are converted into coherent responses. It outlines the foundational processes, emphasizing that the core mechanics are based on linear algebra, probability, and sophisticated architectures, specifically the Transformer model. Key to this understanding is the tokenization process, where text is broken down into subword units, followed by the creation of embeddings that capture semantic relationships in a high-dimensional vector space. Techniques like Byte Pair Encoding (BPE) and positional encoding are highlighted as essential steps for LLMs to comprehend and generate language. This exploration is significant for the AI/ML community as it delineates the intricate elements behind LLM functionality, expanding knowledge of architectural innovations like self-attention and multi-head attention that empower these models to discern context and relationships among words in a sentence. The implications of these insights are profound; as the architecture evolves (including advancements like Rotary Position Embeddings), so too does the potential for developing more advanced AI solutions capable of nuanced language understanding, setting the stage for future advancements in AI-driven software development.