Introduction to LLM

This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.

Total of 14 articles available. | Currently on page 1 of 1.

Chapter 9 — Model Integrity and Supply Chain Risks

Ninth post of the LLM Primer VII walkthrough. Open-source model dependency risk, Sleeper Agents (Hubinger et al.), safetensors vs pickle, CVE-2024-3568, and the SLSA / Sigstore artifact-signing discipline.

2026-05-18

Chapter 7 — Hallucinations and Reliability

Seventh post of the LLM Primer VII walkthrough. Why hallucinations occur, the confidence-vs-correctness gap, and hybrid verification architectures — anchored by the Moffatt v Air Canada and Mata v Avianca cases.

2026-05-16

Chapter 6 — Retrieval-Augmented Generation Risks

Sixth post of the LLM Primer VII walkthrough. Trust boundaries in RAG, malicious document injection, PoisonedRAG and BadRAG, and monitoring retrieval flows for the attacker's fingerprints.

2026-05-15

LLM Primer VII — Series Introduction & Index

Kicking off the chapter-by-chapter walkthrough of Book VII in the LLM Primer series — AI Security. Why in LLM systems code and data are the same string, and the schedule for the seventeen posts that follow, May 10 through May 26. This is the series finale.

2026-05-09

Chapter 8 — Optimizing Performance, Serving, and Cost

Eighth and final post of the LLM Primer V walkthrough. Semantic caching, dynamic model routing, and what actually happens inside the inference server — plus a look ahead to Volume VI on scaling.

2026-04-21

Chapter 4 — AI Agents and Tool Calling

Fourth post of the LLM Primer V walkthrough. ReAct loops, tool schemas as contracts, and the three memory layers agents actually need in production.

2026-04-17

Chapter 1 — The Discipline of AI Engineering

First post of the LLM Primer V walkthrough. Why the demo works and production doesn't — the deterministic wrapper around the probabilistic core, and the five pillars (reliability, quality, performance, cost, evolution) that keep the wrapper honest.

2026-04-14

LLM Primer V — Series Introduction & Index

Kicking off the chapter-by-chapter walkthrough of Book V in the LLM Primer series — Building Real-World LLM Applications. Why AI engineering is a discipline of its own, who this book is for, and the schedule for the eight posts that follow, April 14 through April 21.

2026-04-13

Chapter 6 — Fundamental Orchestration Strategies

Sixth post of the LLM Primer IV walkthrough. The two foundational orchestration shapes — sequential pipelines and concurrent scatter-gather — and the prior question every team should ask: is a multi-agent system the right answer at all?

2026-04-04

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 5 — Training Large Models: What Actually Goes Into a Frontier Model

Chapter 5 of the LLM Primer I series. How frontier LLMs are actually trained — the data pipeline, the loss function, the months of GPU time, and why "training" is now an industrial-scale engineering problem more than a research problem. Demystifies what those hundred-million-dollar training runs are paying for.

2026-02-22