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

Chapter 16 — Secure Fine-Tuning and Adaptation

Sixteenth post of the LLM Primer VII walkthrough. Why fine-tuning aligned models degrades safety (Qi et al.), poisoned fine-tuning data, and rollback disciplines that keep the safety envelope intact.

2026-05-25

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 8 — Adversarial Attacks on Models

Eighth post of the LLM Primer VII walkthrough. Adversarial examples in NLP (HotFlip, TextFooler), model extraction (Tramèr et al., Carlini et al.), and the defensive strategies for API-boundary abuse.

2026-05-17

Chapter 3 — Data Security and Privacy

Third post of the LLM Primer VII walkthrough. Training-data risks, memorization and extraction (Carlini et al., Nasr et al.), and the encryption, isolation, and retention disciplines that keep sensitive prompts contained.

2026-05-12

Chapter 15 — Serverless APIs vs Dedicated Infrastructure

Fifteenth post of the LLM Primer VI walkthrough. The breakeven math between serverless APIs and dedicated infrastructure, the hidden platform-engineering overhead each side takes on, and microVM sandboxes for agent code execution.

2026-05-07

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

Chapter 13 — Autoscaling and Cold-Start Mitigation

Thirteenth post of the LLM Primer VI walkthrough. Why standard HPA fails for LLM serving, KEDA for TTFT-aware scaling, Knative scale-to-zero, and CRIU / CUDA graph caching for sub-5-second cold starts.

2026-05-05

Chapter 12 — Disaggregated Serving and Kubernetes

Twelfth post of the LLM Primer VI walkthrough. Why aggregating prefill and decode wastes compute, and how LeaderWorkerSet, NVIDIA Grove, and KAI Scheduler split them apart on Kubernetes.

2026-05-04

Chapter 11 — The Platform and Orchestration Layer

Eleventh post of the LLM Primer VI walkthrough. Engine vs platform — Ray Serve, KServe, BentoML, and NVIDIA Triton — and where each fits in a multi-model pipeline.

2026-05-03

Chapter 10 — The LLM Engine Layer

Tenth post of the LLM Primer VI walkthrough. vLLM as the safe default, TensorRT-LLM for peak NVIDIA-only throughput, SGLang for structured and agentic outputs, and TGI/Ollama for the rest.

2026-05-02

Chapter 9 — Speculative Decoding

Ninth post of the LLM Primer VI walkthrough. The draft-verify paradigm — EAGLE, Medusa, MTP, Lookahead, N-gram — and the verification bottleneck that decides real speedup.

2026-05-01

Chapter 8 — Next-Generation KV Cache Management

Eighth post of the LLM Primer VI walkthrough. PagedAttention, KV eviction algorithms (H2O, InfiniGen), and prefix caching for multi-turn conversations and multi-agent RAG.

2026-04-30

Chapter 7 — Advanced Batching Strategies

Seventh post of the LLM Primer VI walkthrough. Static vs dynamic vs continuous (in-flight) batching, iteration-level scheduling, and how a batch's slots actually progress on the GPU.

2026-04-29

Chapter 6 — Pruning and Knowledge Distillation

Sixth post of the LLM Primer VI walkthrough. Structured vs unstructured pruning, 2:4 sparsity on Hopper, and the distillation lineage from soft probabilities to Patient Knowledge Distillation and MiniLLM.

2026-04-28

Chapter 4 — Specialized AI Silicon and ASICs

Fourth post of the LLM Primer VI walkthrough. Groq LPUs, AWS Inferentia2, Google TPUs, and Intel Gaudi — where specialized silicon fits alongside general-purpose GPUs.

2026-04-26

Chapter 3 — Data Center GPUs for Generative AI

Third post of the LLM Primer VI walkthrough. The NVIDIA lineup (H100, H200, B200, L40S) vs AMD MI300X — and why HBM bandwidth matters more than FLOPs for decoding.

2026-04-25

Chapter 2 — The KV Cache Challenge

Second post of the LLM Primer VI walkthrough. The KV cache formula, the attention-variant trade-offs (MHA vs GQA vs MQA), and the memory-fragmentation problem PagedAttention solves.

2026-04-24

Chapter 1 — The Mechanics of Token Generation

First post of the LLM Primer VI walkthrough. The autoregressive bottleneck, the prefill/decode split, and why a high-end GPU is 99.7% idle while serving a single user.

2026-04-23

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 7 — LLM Security and Guardrails

Seventh post of the LLM Primer V walkthrough. The OWASP LLM Top 10 as a working checklist, direct-versus-indirect prompt injection, and the four-layer mitigation matrix.

2026-04-20

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 8 — Architectural Deployment Layouts

Eighth post of the LLM Primer IV walkthrough. The three deployment layouts that have emerged in the MCP ecosystem — reusable agent, strict purity, hybrid — and the four binding constraints that determine which one fits which project.

2026-04-06

Chapter 11 — Continuous Updates and Pipeline Optimization

Eleventh and final post of the LLM Primer III walkthrough. CDC and incremental indexing keep the corpus fresh, semantic caching and model tiering keep latency down, and a four-stage feedback loop closes the gap between what production tells the team and what the team actually changes — plus a bridge to Volume IV on Model Context Protocol.

2026-03-28

Chapter 4 — Selecting the Right Vector Database

Fourth post of the LLM Primer III walkthrough. The architectural split between purpose-built vector databases and Postgres-style extensions, the managed leaders (Pinecone, Vertex), the open-source field (Qdrant, Milvus, Weaviate), the embedded options, and the three operational axes — residency, ops, cost — that decide the real choice.

2026-03-21

Chapter 1 — The Evolution of RAG Architecture

First post of the LLM Primer III walkthrough. The four architectural postures of RAG — Naive, Advanced, Modular, Agentic — read as a story about handing more agency to the LLM one decision at a time, and the honest answer to when fine-tuning is the better tool than retrieval.

2026-03-18

Chapter 7 — Efficiency and Transformer Variants

Seventh post of the LLM Primer II walkthrough. The computational complexity of attention, the GPU memory and throughput math that constrains real systems, FlashAttention derived from first principles, and the family of clever variants — multi-query, gated, low-rank — that keep big models running.

2026-03-09

Chapter 6 — Transformer Blocks and Representation Power

Sixth post of the LLM Primer II walkthrough. Feed-forward layers, activation functions, why "attention + FFN" is exactly the right pair, and what mathematical guarantees depth and width give you about expressivity.

2026-03-08

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 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

Chapter 6 — Fine-Tuning & Adaptation: From Raw Model to Helpful Assistant

Chapter 6 of the LLM Primer I series. The full adaptation stack — from cheap prompt-based steering to parameter-efficient fine-tuning to full alignment with RLHF and its modern successors like DPO. Why post-training is now where closed-model APIs actually differentiate.

2026-02-23

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

Chapter 3 — Neural Networks for Language: From RNNs to Self-Attention

Chapter 3 of the LLM Primer I series. Why feedforward networks couldn't handle language, how RNNs hit a wall, and what attention changed. A clean conceptual progression through the three neural-network shapes that defined modern NLP — without the math anxiety.

2026-02-20

The LLM Primer Series — A Field Guide to Generative AI, Built One Volume at a Time

The LLM Primer Series — a completed seven-volume field guide to generative AI by Sho Shimoda. From foundations to security. Includes Physical AI as sister volume. All 7 volumes available on Amazon.

2026-02-15

Part I — Mathematical Foundations for Understanding LLMs

A clear and intuitive introduction to the mathematical foundations behind Large Language Models (LLMs). This section explains probability, entropy, embeddings, and the essential concepts that allow modern AI systems to think, reason, and generate language. Learn why mathematics is the timeless core of all LLMs and prepare for Chapter 1: Mathematical Intuition for Language Models.

2025-09-02