Day 27 of 30Artificial Intelligence

🗣️AI for Language & Images

🎯 Today's objective

Use powerful pre-trained models without training from scratch.

📖 Key concepts

Pre-trained models save time/data
LLMs predict the next token
Computer vision classifies/detects in images
Tokens & embeddings turn data into vectors

💻 Try this code

# Concept: text -> tokens -> numbers (embeddings) -> model -> output
# Using a hosted model (pseudo-code):
# reply = call_llm("Summarise this paragraph: ...")
# print(reply)

👥 Learn it your way

🧒 Young Explorers

You don’t have to build the brain — you can borrow a giant one and ask it questions.

Your task

Write 3 things an image model and a language model each do well.

💼 Professionals

Know when to use an API vs fine-tune vs train; understand embeddings & RAG at a high level.

Your task

Sketch an architecture: user question → embed → retrieve docs → LLM answer (RAG).

🤖 AI connection

This is how most real AI products are built today — composing pre-trained models via APIs rather than training from zero.

✅ Quick check

What does an LLM fundamentally predict?

The next token (piece of text).

Why use a pre-trained model?

It saves huge amounts of data, time, and compute.