Large Language Models (LLMs) have taken the world by storm with their impressive ability to generate human-like text, answer questions, and even code. However, it's essential to understand that these AI marvels are not without their limitations. One crucial aspect that often goes overlooked is how LLMs handle memory and the concept of "context windows."
LLMs are not rule-based systems but rather function more similarly to the human brain, relying on vast interconnected data points and context to generate responses. This necessitates a shift from issuing commands to guiding the LLM through prompts and understanding its responses as associative outputs.
