RAG enhances LLM technology by incorporating external data sources in real-time, providing more comprehensive and contextually relevant responses6. It dynamically updates the pool of information it accesses, offering responses informed by the most up-to-date knowledge without needing to re-train the model.
LLMs are compared to young children or Alzheimer's patients due to the way they produce output. Like young children or Alzheimer's patients, LLMs can generate responses that seem to have some coherence but may not always be entirely accurate or grounded in reality. This comparison, however, is misleading as it suggests a level of cognition and intent that LLMs do not possess. Instead, their output is more akin to bullshitting, where there is no regard for the truth or connection to reality, and is only intended to serve the immediate situation.
In the context of Large Language Models (LLMs), the term 'hallucination' refers to the generation of plausible-sounding but factually incorrect or nonsensical information. It occurs when the model, despite its impressive language skills, fails to accurately represent or reason about the real world, often resulting in the production of false or misleading content.