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 57 articles available. | Currently on page 1 of 2.

Chapter 17 — Future Threats and Emerging Defenses

Seventeenth post of the LLM Primer VII walkthrough — and the series finale. Agent risks and the lethal trifecta, multimodal attack surfaces, deepfakes and C2PA provenance, plus a closing map of the whole LLM Primer arc and the Physical AI sister volume.

2026-05-26

Chapter 14 — Bias, Fairness, and Responsible AI

Fourteenth post of the LLM Primer VII walkthrough. Sources of bias in LLMs, measurement (BBQ, BOLD, StereoSet, HELM), and the safety-utility trade-off honestly named.

2026-05-23

Chapter 12 — Access Control and Identity

Twelfth post of the LLM Primer VII walkthrough. OAuth 2.0 + PKCE, ABAC vs ReBAC (Zanzibar), multi-tenant isolation, and token-bucket rate limits for LLM APIs.

2026-05-21

Chapter 11 — Observability, Logging, and Incident Response

Eleventh post of the LLM Primer VII walkthrough. Structured LLM logging with PII redaction, OpenTelemetry GenAI conventions, and the NIST SP 800-61 IR cycle adapted for probabilistic systems.

2026-05-20

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 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

Chapter 5 — Input Validation and Output Filtering

Fifth post of the LLM Primer VII walkthrough. Input sanitization, structured guardrails (NeMo, Llama Guard 3, Lakera, Bedrock), and red teaming with Garak, PyRIT, and promptfoo.

2026-05-14

Chapter 4 — Prompt Injection and Jailbreaks

Fourth post of the LLM Primer VII walkthrough. Prompt injection as a structural consequence, the jailbreak taxonomy (DAN, grandma, Zou et al. suffixes, Crescendo, Skeleton Key), and the four-layer mitigation matrix.

2026-05-13

Chapter 1 — Why AI Security Is Different

First post of the LLM Primer VII walkthrough. Why LLM security is structurally different from traditional security — the collapsed code/data boundary, the probabilistic core, and the OWASP LLM Top 10 as a working checklist.

2026-05-10

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 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 5 — Demystifying Quantization

Fifth post of the LLM Primer VI walkthrough. From BF16 to INT4 to Blackwell FP4 — quantization algorithms (AWQ, GPTQ, GGUF, SmoothQuant), NVIDIA ModelOpt, and when quantization is safe versus lossy.

2026-04-27

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 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 3 — Retrieval-Augmented Generation

Third post of the LLM Primer V walkthrough. The RAG pipeline end to end — chunking, hybrid retrieval, query transformation, multimodal, and text-to-SQL — and where RAG fits versus fine-tuning and long context.

2026-04-16

Chapter 2 — Foundation Models & Prompt Engineering

Second post of the LLM Primer V walkthrough. Model tiering, sampling parameters, defensive prompt patterns, and structured outputs as engineering surfaces — the layer just inside the deterministic wrapper.

2026-04-15

Chapter 14 — Benchmarking, Testing, and Performance

Fifteenth and final post of the LLM Primer IV walkthrough. The MCP-Universe Benchmark on real servers, the two systemic failure modes it exposed, the ten-times throughput gap between session-per-request and shared session pools, and the bridge to Volume V.

2026-04-12

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

LLM Primer III — Series Introduction & Index

Kicking off the chapter-by-chapter walkthrough of Book III in the LLM Primer series — Enhancing Enterprise AI with RAG. Why retrieval-augmented generation looks simple from the outside and is a stack of disciplines underneath, who this book is for, and the schedule for the eleven posts that follow, March 18 through March 28.

2026-03-17

Chapter 13 — Limitations, Risks, and Open Challenges

Eleventh post of the LLM Primer II walkthrough. The honest chapter — the compute and energy ceilings that constrain the field, the biases that scale with the data, and the ethical and societal questions that math alone cannot answer.

2026-03-15

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

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 4 — Attention: The Core Mechanism

Fourth post of the LLM Primer II walkthrough. Self-attention derived from intuition, the geometry of queries/keys/values, multi-head structure and normalization, softmax in detail with its temperature knob, and a striking final move: attention seen as a kernel method.

2026-03-06

Chapter 1 — Mathematical Intuition for Language Models

First post of the LLM Primer II walkthrough. Mathematical notation without intimidation, probability for language generation explained from scratch, and entropy as a way to measure uncertainty — the trio that makes the rest of the book readable.

2026-03-03

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

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

A Chapter-by-Chapter Walkthrough of LLM Primer I — Series Introduction & Index

Introduction and index for the twelve-part chapter-by-chapter walkthrough of LLM Primer I: How Generative AI Works. One post per day, Feb 18 through March 1, 2026. Read them in order or pick the chapter that matters most to you. All twelve are listed and linked here.

2026-02-17

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

1.3 Entropy and Information: Quantifying Uncertainty

A clear, intuitive exploration of entropy, information, and uncertainty in Large Language Models. Learn how information theory shapes next-token prediction, why entropy matters for creativity and coherence, and how cross-entropy connects probability to learning. This section concludes Chapter 1 and prepares readers for the conceptual foundations in Chapter 2.

2025-09-06

1.1 Getting Comfortable with Mathematical Notation

A clear and accessible guide to understanding the mathematical notation used in Large Language Models. Learn how tokens, sequences, functions, and conditional probability expressions form the foundation of LLM reasoning. This chapter prepares readers for probability, entropy, and information theory in later sections.

2025-09-04

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