LLM Fundamentals
How large language models actually work — tokens, transformers, context windows, and why they make things up.
LLM Basics
The core ideas behind every AI model you'll ever call.
BEGINNERWhat Is a Large Language Model (LLM)? A Plain-English GuideBEGINNERHow Do LLMs Actually Work? Next-Token Prediction ExplainedINTERMEDIATEWhy Do LLMs Need GPUs? AI Compute Explained for BeginnersINTERMEDIATEWhat Are AI Scaling Laws? Why Bigger Models Got SmarterBEGINNERHow Does ChatGPT Work? A Plain-English Explanation
Tokens & Tokenization
The unit everything is priced, limited, and measured in.
Transformers & Attention
The architecture behind every modern model, without the math degree.
How Text Generation Works
From logits to the next word: how an LLM actually turns your prompt into text, one token at a time.
BEGINNERWhat Is Next-Token Prediction? How LLMs Actually Generate TextINTERMEDIATEWhat Is Autoregressive Generation? How LLMs Write One Token at a TimeINTERMEDIATELogits in an LLM: Raw Scores to a Logits Probability DistributionINTERMEDIATESoftmax Function Machine Learning Guide: Turning Scores Into ProbabilitiesINTERMEDIATEToken Sampling vs Greedy Decoding: How an LLM Picks the Next Token
Context Windows & Model Memory
What a model can hold in its head at once — and what happens when it can't.
Sampling, Temperature & Hallucination
Why the same prompt gives different answers, and why some of them are wrong.