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 6 articles available. | Currently on page 1 of 1.

Chapter 14 — Token Economics and API Pricing

Fourteenth post of the LLM Primer VI walkthrough. The input-vs-output token asymmetry, the hidden cost of conversation history, and the invisible reasoning tokens that quietly rewrite the daily bill.

2026-05-06

LLM Primer VI — Series Introduction & Index

Kicking off the chapter-by-chapter walkthrough of Book VI in the LLM Primer series — Scaling AI Systems. Why inference is the discipline that decides whether an LLM app survives real users, and the schedule for the sixteen posts that follow, April 23 through May 8.

2026-04-22

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 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 11 — Cutting-Edge Research: MoE, Reasoning Models, and the New Scaling Axis

Chapter 11 of the LLM Primer I series. The research frontiers that are now production reality — mixture-of-experts, retrieval-augmented memory, native multimodal tokenization, continual learning, and the inference-time scaling paradigm that produced today's reasoning models. The 2026 edition's biggest content addition.

2026-02-28

Chapter 9 — Performance, Scaling, and Costs: The Real Engineering Trade-offs

Chapter 9 of the LLM Primer I series. The operational realities of running LLMs at scale — model size vs capability, the latency–throughput trade-off, cost economics, quantization, and edge deployment. Why frontier-tier models are often the wrong choice even when you can afford them.

2026-02-26