Advanced LLM Engineering

A deep-dive walkthrough of what is under the hood of large language models and how that knowledge changes the way you build with them — covering transformer architecture, self-attention and multi-head attention, positional encoding, tokenization algorithms, context window management, the KV cache, temperature/top-K/top-P sampling, quantization, distillation, fine-tuning vs. RAG, LoRA and QLoRA, small language models, open-weight vs. proprietary models, multimodal LLMs, and model routing and fallback — with C# code for the mechanics, back-of-envelope calculators, and interview-style questions.

Table of Contents

Introduction

Transformer Architecture

Self-Attention and Multi-Head Attention

Positional Encoding

Tokenization Algorithms

Context Window Management

KV Cache

Temperature, Top-K, and Top-P Sampling

Model Quantization

Model Distillation

Fine-Tuning vs. RAG

LoRA and QLoRA

Small Language Models (SLMs)

Open-Weight vs.…