What's an LLM? (engineerofwealth.com)

In a charming comic-style explanation, Geek, an engineer, describes Large Language Models (LLMs) to his daughter Bonnie using a series of simplified diagrams. He illustrates how these models are trained on vast amounts of text data, effectively acting like a "giant baby" that learns to predict the next word in a sentence based on its training. However, Bonnie quickly identifies a critical flaw: if the model bases its responses on widely accepted but incorrect information, it can reinforce those errors and produce misleading outputs, humorously dubbing it a "Large Lying Munchkin." This exchange highlights significant concerns within the AI/ML community regarding the concept of "hallucination," where models confidently generate incorrect information due to their training on flawed datasets. Notably, Geek explains that LLMs utilize a context window for generating text but have limited memory, leading to potential omissions and the production of unreliable summaries. As these models evolve and become more integrated into applications, understanding these limitations and intricacies becomes increasingly essential for developers and users alike to harness their potential responsibly.