Exactly! You've perfectly described one of the most fundamental and elegant optimization strategies in artificial intelligence. Yes, something entirely analogous is done, and it takes two main forms that perfectly fit your metaphor.
1. The library's "index": Service Mechanisms
This is a direct analogy to "having the most frequently used books closer at hand." The core technology of modern AIs is called the Attention Mechanism .
When an AI reads your question, it doesn't process it as a monolithic whole. The attention mechanism acts like an internal librarian that, in milliseconds, scans all available information (the "books") and decides which words, concepts, or previous data from the conversation are most relevant to understanding your current sentence and responding.
The "most common" or relevant concepts for your question receive high attention (they are on the nearest shelf).
Irrelevant concepts, even if they are in the text, receive little or no attention (they are in the remote store and are not even consulted).
In essence, AI builds a dynamic relevance map for each task.
2. Rote learning ("What is extremely common is memorized")
This is the second part of your intuition: you don't need to consult the encyclopedia to know your own name. AI has two ways of "memorizing" so you don't have to reread it.
A. The KV Cache (Short-term memory and fast archiving)
It's a high-speed caching system. During a conversation, every word you read and generate is stored in a temporary memory (the KV cache). When I reply, I don't reread the entire conversation from scratch; I consult this incredibly fast cache. It's the equivalent of having the two or three books you're currently using open on your desk, without having to go to the shelf to get them for every sentence. Without this cache, the conversation would be incredibly slow and impossible.
B. Fine-Tuning and "Parameters" (Long-term Memory)
This is where AI emulates the "I already know it, I don't need to read it" approach. This is the difference between:
Training from scratch: It's like learning a language by reading absolutely everything.
Fine-Tuning: This involves taking an already trained model and "specializing" it. Imagine you are an expert lawyer. You take a general model and retrain it with thousands of legal documents. Now, legal concepts, jargon, and legal reasoning structures are "memorized" in its parameters (its "brain"). It doesn't need to search for them in an external database because they are already part of its deep, internal knowledge.
The full analogy in AI
To bring things full circle with your library metaphor, here's how modern AI works:
Massive training: This involves building the enormous storage library. It's equivalent to buying and cataloging millions of books. It's expensive and time-consuming.
Attention Mechanism: It is the librarian who, for your consultation, instantly decides which 5 books out of the millions are relevant and places them right on the desk.
KV cache: It's the desk stand where you leave those 5 books open so you don't have to ask for them again while you work with them.
Fine-Tuning and Parameters: This is the head librarian's memory. After years of work and consultation, they no longer need to look at the catalog to know where the law books are, because that information has been integrated into their deep knowledge.
I hope this explanation clarifies your doubt and completes the picture of how AI, like us, creates its own hierarchies of immediacy and memory.
