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:
- Feed them LOTS of text (books, Wikipedia, code, websites)
- Ask: "Predict the next word"
- If wrong, adjust the model
- 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.
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