One of the biggest misconceptions about Large Language Models (LLMs) is that they "learn" about you the same way a human does. In reality, LLMs use three distinct types of memory to process information and answer your questions.

1. Training Memory (Parametric Knowledge)

This is the model's "innate" knowledge. It is everything the AI learned during its massive training phase before it was released. Think of this as the model's long-term memory or its general education.

Key Concept: This memory is "frozen". The model cannot learn new facts or remember your name permanently through this method. To change this memory, the model would need to be retrained.

2. The Context Window (Working Memory)

The Context Window is the model's short-term memory. It represents the specific text the model can "see" and process during your current conversation.

Key Concept: This memory is limited. Every time you send a message, the model re-reads the previous parts of the chat. If the conversation becomes too long, the oldest information falls out of the "window" and is forgotten.

3. RAG (External Memory)

Retrieval-Augmented Generation (RAG) is like giving the AI a library card. Since the AI cannot remember everything in its training or fit everything in its context window, it looks up specific information from an external database when needed.

Key Concept: This allows the AI to access real-time data or private documents without needing to be retrained. It finds the relevant "file," reads it, and places that info into the Context Window temporarily.

What's Next?

Understanding these three layers is the secret to getting better results from AI. In our next tutorial, we will dive deep into Context Windows to learn how to manage short-term memory effectively.

After that, we will explore RAG to see how AI can interact with massive amounts of external data.