Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 11 — Evaluation, Calibration, and Inference
Eleventh post of the LLM Primer II walkthrough. Perplexity, calibration, the error bars that every benchmark score should carry, and the mathematics of measuring hallucination — the chapter where we ask how anyone can measure a machine that can say anything.
2026-03-13
Chapter 10 — Post-Training and Alignment Mathematics
Tenth post of the LLM Primer II walkthrough. The mathematics that civilizes a brilliant but feral next-word predictor into a helpful assistant — supervised fine-tuning, reward modeling, RLHF on a KL leash, and the elegant DPO derivation that collapses the whole pipeline into a single supervised loss.
2026-03-12
LLM Primer II — Language Models Through Mathematics: Series Introduction & Index
Kicking off the chapter-by-chapter walkthrough of Book II in the LLM Primer series — Language Models Through Mathematics. How the book is organized, what each chapter delivers, and the schedule for the fourteen posts that follow, March 3 through March 16.
2026-03-02
Chapter 7 — Beyond Next-Token Prediction: Embeddings, Retrieval, and Multimodality
Chapter 7 of the LLM Primer I series. The capabilities that turn a next-token predictor into something much more — embeddings, semantic search, retrieval-augmented generation, and the move into multimodal inputs. How RAG actually keeps an LLM grounded in real documents instead of confabulating.
2026-02-24
The LLM Primer Series — A Field Guide to Generative AI, Built One Volume at a Time
The LLM Primer Series — a seven-volume field guide to generative AI by Sho Shimoda. Each volume covers a different layer of working with large language models, from foundations to scaling to security. This is the landing page: an overview of the whole series, plus the live chapter-by-chapter walkthrough of the first volume.
2026-02-15