Explaining AI, ML, LLMs, RAG, and Multi-Modal AI Using a Library Example

I've already used this analogy a few times as I teach people what I'm learning about AI and Machine Learning! Feel free to share this as well for those who are brand new to AI and want to understand it better. Also please follow me on my journey at www.SolomonChrist.com and watch my YouTube videos (https://www.youtube.com/@SolomonChristAI).

1. The Library of Books (LLM)

Think of a Large Language Model (LLM) as a huge library of books that have already been written.

• These books cover a wide range of topics and provide pre-existing knowledge.
• When you ask a question, the LLM finds the most relevant book and gives an answer based on what’s inside.
• However, once the books are written (model trained), you cannot add new books unless you rebuild the library (retrain the model).
• Example: ChatGPT, Gemini, Claude, and other LLMs work this way—they generate responses based on previously learned data.

2. RAG – The Research Librarian

A Retrieval-Augmented Generation (RAG) system is like having a research librarian in the library.

• Instead of only relying on the books that are already there, the librarian can bring in new books, print new articles, and fetch the latest information from the internet before answering your question.
• This means the system can give up-to-date and more relevant responses without retraining the entire model.
• Example: If an LLM (library) was last trained in 2021, it cannot know what happened in 2024. But if it has a RAG system (a librarian), it can search online, check a database, or even read a new PDF file before answering.
• This is why AI search engines like Perplexity AI and ChatGPT with Browsing use RAG—they fetch real-time data!

3. Modals & Multi-Modal AI – Different Formats in the Library

Now let’s think about modalities (inputs and outputs).

• A single-modal AI is like a library that only has books (text)—it can only take in and give out text.
• A multi-modal AI is like a library that has: Books (text input/output), Audiobooks (audio input/output), Movies (video input/output), Images (visual input/output) and it can take these as both inputs and outputs or single sided inputs (like text only to anything else) =>

?? Example of Single Sided Modal Input (Text to Multi Modal Output):

• Text-to-Text AI → Reading a book and summarizing it.
• Text-to-Image AI → Reading a book and creating an illustration.
• Text-to-Audio AI → Turning a book into an audiobook.
• Text-to-Video AI → Creating a movie based on a book’s story.

Some allow for multi-modal inputs and multi-modal outputs. Some allow a combination as well.

Modern AI models like GPT-4, Gemini, and Mistral are becoming multi-modal, meaning they can handle multiple types of data.

4. Complete Analogy Recap

• LLM (Library of Books) → A collection of knowledge that cannot be updated unless new books are written (retraining).
• RAG (Research Librarian) → A librarian who can fetch new books, research papers, and real-time articles to provide better answers.
• Multi-Modal AI (Different Media in the Library) → A library that has books, audiobooks, movies, images, and more—allowing different types of input and output.

5. Final Goal => RAG and Modals => LLM Base Model = APP YOU NEED

• When getting into AI applications, understand what your final goal is.
• ChatGPT is NOT the only AI App and there are better apps based on your specific needs
• Choose your FINAL GOAL first (Ex. Image creation), then understand what RAG items and Modals you need. Do you need to share a PDF for additional information and require voice chat entry? Or do you want to supply an image and get an output as a video?
• Based on that step, NOW you look for LLMs that specialize in those areas. For example, for image generation, Midjourney has been a great system for a long time. There are many others as well, but you can see now how choosing the right LLM will get you a BETTER result.
• Finally now that you know the LLM you need and Modals and RAG items you want to have, NOW you go to websites like https://www.theresanaiforthat.com and look for apps that are based on that specific LLM.
• FULL EXAMPLE:

a. I want an image that is high quality (Final Goal) => I want to provide a text input and get an image output (Modal types and/or RAG requirements) => LLMs for Images (DALL·E 3, Stable Diffusion, Midjourney) => Now I search everywhere for apps that use that LLM for its base and see if I like them.

The above will help you get BETTER results rather than just using ChatGPT for everything and then wondering why others are getting much more amazing results with different systems.

Hope this helps you all out in joining the AI + ML and Automation Ecosystem with me and my friends! Go ahead and share this document with friends and family who are just wrapping their heads around AI and Machine Learning. Please make sure to follow me along on YouTube and on my website as well!

Website: https://www.SolomonChrist.com

YouTube: https://www.youtube.com/@SolomonChristAI