While LLMS have revolutionized the way we interact with technology, they come with some significant limitations:

LLMs sometimes hallucinate, meaning they provide factually incorrect answers. This occurs because they generate responses based on patterns in the data they were trained on, not always on verified facts.

Models like GPT-4 have a knowledge cutoff date (e.g., May 2024). They lack information on events or developments that occurred after this date.

LLMs often provide answers without clear sources, leading to untraceable reasoning.

While LLMs are good at generating general responses, they often lack domain-specific expertise.

Imagine RAG as your personal assistant who can memorize thousands of pages of documents. You can later query this assistant to extract any information you need.

By integrating RAG, we can overcome many limitations of traditional LLMs, providing more accurate, up-to-date, and domain-specific answers.

In upcoming posts, we'll explore more advanced topics on RAG and how to obtain even more relevant responses from it. Stay tuned!