🧠 LLMs Explained Like You're 5

A very well-read librarian

Day 11 of 149

👉 Full deep-dive with code examples

The Librarian Analogy

Imagine a librarian who has:

• Read every book in the library
• Memorized patterns of how language works
• Can predict what word comes next in a sentence

You ask: "The capital of France is ___"
Librarian: "Paris"

LLMs are librarians trained on huge amounts of text (including lots of internet text).

What LLM Stands For

Large Language Model

• Large → Billions of parameters (memory)
• Language → Trained on text
• Model → Mathematical prediction engine

How They Work (Simply)

LLMs just predict the next word:

Input: "The cat sat on the"
LLM thinks: What word typically follows this?
Output: "mat" (high probability)

String enough predictions together, and you get:

• Essays
• Code
• Poems
• Conversations

The Training

To predict well, they learn by:

1. Feed them LOTS of text (books, Wikipedia, code, websites)
2. Ask: "Predict the next word"
3. If wrong, adjust the model
4. Repeat billions of times

After training, they've learned patterns of language.

Famous LLMs

• GPT-4 (OpenAI)
• Claude (Anthropic)
• Gemini (Google)
• Llama (Meta)

In One Sentence

LLMs are AI models trained on massive text to predict what comes next, enabling them to write, answer questions, and code.

🔗 Enjoying these? Follow for daily ELI5 explanations!

Making complex tech concepts simple, one day at a time.