Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
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 15 — Building a Secure AI Organization
Fifteenth post of the LLM Primer VII walkthrough. Security culture for AI teams, red teams and internal audits, vendor risk (SOC 2, ISO 42001), and the emerging AI BOM.
2026-05-24
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 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 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
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 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 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 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
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 6 — AI Observability and Tracing
Sixth post of the LLM Primer V walkthrough. OpenTelemetry GenAI conventions, span design for LLM apps, cost tracking, and the loop back into the evaluation harness.
2026-04-19
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 13 — Frameworks and Cloud Integration
Fourteenth post of the LLM Primer IV walkthrough. Strands with Bedrock, the AWS state-layer pattern, the Microsoft Agent Framework, LangChain, Semantic Kernel — and the three production integration shapes teams keep arriving at independently.
2026-04-11
Chapter 2 — Unveiling the Model Context Protocol (MCP)
Second post of the LLM Primer IV walkthrough. What MCP actually standardizes, the three-role split of Host, Client, and Server, why dynamic discovery and bidirectional messaging differ from REST in the cases that matter, and the session lifecycle that opens with capability negotiation.
2026-03-31
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 10 — Leading Evaluation Frameworks
Tenth post of the LLM Primer III walkthrough. A field guide to the frameworks that turn the Evaluation Triad into something a team can actually run — RAGAS, TruLens, DeepEval on one side, Braintrust, LangSmith, Phoenix, Galileo, Opik on the other, and the Evaluation Gap none of them has yet closed.
2026-03-27
Chapter 8 — Data Anonymization in the RAG Pipeline
Eighth post of the LLM Primer III walkthrough. Pre-generation versus post-generation anonymisation, the three technique families — masking, synthetic replacement, differential privacy — and the utility-privacy tradeoff that determines whether the system remains useful at all.
2026-03-25
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 14 — Practical Knowledge for Engineers
Twelfth post — the closing chapter of the LLM Primer II walkthrough. How to keep deepening your understanding after the book ends, the tools and libraries that turn the math into shipping work, and the bridge to the other books in the LLM Primer series.
2026-03-16
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 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 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 9 — Training at Scale
Ninth post of the LLM Primer II walkthrough. How data preprocessing quietly shapes everything that follows, the mathematics of mini-batch learning and parallelism, and the surprisingly subtle question of how to keep a training run numerically stable across thousands of GPUs.
2026-03-11
Chapter 8 — How Models Learn
Eighth post of the LLM Primer II walkthrough. Why over-parameterized models generalize at all, the implicit bias of gradient-based optimization, the empirical scaling laws that forecast capability before training, and the open mathematical questions that still surround LLM theory.
2026-03-10
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
Chapter 3 — Mathematical Tools for Language Models
Third post of the LLM Primer II walkthrough. The probability and statistics you actually need for language modeling, the slice of linear algebra that matters, and embeddings as the first place those two tools meet inside an LLM.
2026-03-05
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 12 — Building Your Own LLM System: From Datasets to Production
Chapter 12 of the LLM Primer I series. The final chapter. What it actually takes to build an LLM-powered system end to end — dataset licensing, training pipelines, evaluation frameworks, the integrated application stack, and the case-study patterns that distinguish successful deployments from failed pilots.
2026-03-01
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 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 4 — The Transformer Architecture: Inside the Engine of Modern AI
Chapter 4 of the LLM Primer I series. A tour of the Transformer block — how self-attention, positional encoding, and stacked layers combine to produce the architecture every modern LLM is built on. Includes a clear explanation of why scaling Transformers works, and what it costs.
2026-02-21
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
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.
2026-02-152.1 What Is a Large Language Model?
A clear and in-depth explanation of what Large Language Models (LLMs) are. Learn how LLMs map token sequences to probability distributions, why next-token prediction unlocks general intelligence, and what makes a model “large.” This section builds the foundation for understanding pretraining, parameters, and scaling laws.
2025-09-08Chapter 2 — LLMs in Context: Concepts and Background
An accessible introduction to Chapter 2 of Understanding LLMs Through Math. Explore what Large Language Models are, why pretraining and parameters matter, how scaling laws shape model performance, and why Transformers revolutionized NLP. This chapter provides essential context before diving deeper into the mechanics of modern LLMs.
2025-09-071.1 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.
2025-09-04Chapter 1 — Mathematical Intuition for Language Models
An accessible introduction to Chapter 1 of Understanding LLMs Through Math. Learn how mathematical notation, probability, entropy, and information theory form the core intuition behind modern Large Language Models. This chapter builds the foundation for understanding how LLMs generate text and quantify uncertainty.
2025-09-03Part 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.
2025-09-02Understanding LLMs – A Mathematical Approach to the Engine Behind AI
A preview from Chapter 7.4: Discover why large language models inherit bias, the real-world risks, strategies for mitigation, and the growing role of AI governance.
2025-09-01
7.2 Resource-Efficient Training
A preview from Chapter 7.2: Learn how techniques like distillation, quantization, distributed training, and data efficiency make LLMs faster, cheaper, and greener.
2024-10-08
5.3 Real-Time Deployment Challenges
A preview from Chapter 5.3: Explore latency, scalability, and optimization techniques for deploying large language models in real-time applications.
2024-10-01
5.2 Compute Resources and Cost
A preview from Chapter 5.2: Learn why LLMs demand massive compute power, what drives cost, and practical strategies to optimize performance and sustainability.
2024-09-30
5.1 Bias & Ethical Considerations
A preview from Chapter 5.1 of our book: uncover how large language models inherit bias and learn strategies to build fair, trustworthy AI.
2024-09-29
3.3 Fine-Tuning and Transfer Learning for LLMs: Efficient Techniques Explained
Learn how fine-tuning and transfer learning techniques can adapt pre-trained Large Language Models (LLMs) to specific tasks efficiently, saving time and resources while improving accuracy.
2024-09-14
3.2 LLM Training Steps: Forward Propagation, Backward Propagation, and Optimization
Explore the key steps in training Large Language Models (LLMs), including initialization, forward propagation, loss calculation, backward propagation, and hyperparameter tuning. Learn how these processes help optimize model performance.
2024-09-13
3.1 LLM Training: Dataset Selection and Preprocessing Techniques
Learn about dataset selection and preprocessing techniques for training Large Language Models (LLMs). Explore steps like noise removal, tokenization, normalization, and data balancing for optimized model performance.
2024-09-12