Fundamentals

Concept

AI & LLM Glossary

Fifty essential AI and LLM terms defined in plain English — from agents and embeddings to RAG, RLHF, and vector databases. Your quick-reference cheat sheet.

Concept

What is an LLM?

Large Language Models, explained from first principles — what they are, how they predict tokens, and where the magic and limits come from.

Interview

Explain the bias-variance tradeoff. How do you diagnose which is hurting your model?

Tests whether you understand fundamental model behavior beyond memorized definitions, and whether you can diagnose real-world problems.

Interview

Why do we use cross-entropy loss for classification instead of MSE?

Tests whether you understand the math behind the choice, not just which loss to import.

Interview

Walk me through how Adam works, and when you'd use it vs SGD with momentum.

Tests deeper optimizer knowledge — separates 'studied for the interview' from 'has shipped models'.

Interview

What's the difference between L1 and L2 regularization, and when would you use each?

Quick check on fundamental ML knowledge — and whether you can connect math to practical model behavior.