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

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 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

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 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

Chapter 5 — Evaluating LLM Applications

Fifth post of the LLM Primer V walkthrough. The offline-online eval distinction, LLM-as-judge patterns, the RAG Triad, and trajectory tests for agents.

2026-04-18

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 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 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 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 12 — Real-World Applications of LLMs

Twelfth post of the LLM Primer II walkthrough. Text generation, summarization, QA, translation, reasoning — and the constrained decoding, agent loops, and multimodal generalization that turn one next-token machine into a dozen kinds of product.

2026-03-14

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 2 — LLMs in Context: Concepts and Background

Second post of the LLM Primer II walkthrough. What an LLM actually is, the three things "pretraining, parameters, scale" really stand for, the unusual nature of language as a data source, and why the transformer rewrote the field in a single year.

2026-03-04

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 8 — Using LLMs in Applications: Chatbots, Code, Extraction, and Agents

Chapter 8 of the LLM Primer I series. The application patterns that actually ship in production — chatbots, summarization, code assistants, structured extraction, and the rise of agentic systems where the model drives a tool-use loop. Plus the benchmarks every engineer should recognize by name.

2026-02-25

Chapter 7 — Beyond Next-Token Prediction: Embeddings, Retrieval, and Multimodality

Chapter 7 of the LLM Primer I series. The capabilities that turn a next-token predictor into something much more — embeddings, semantic search, retrieval-augmented generation, and the move into multimodal inputs. How RAG actually keeps an LLM grounded in real documents instead of confabulating.

2026-02-24

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 2 — Probability, Tokens, and Text: The Game of Next-Word Guessing

Chapter 2 of the LLM Primer I series. How LLMs convert text into tokens, why language modeling is fundamentally a probability problem, and how the old n-gram approach gave way to neural models that can generalize. Includes plain-English explanations of perplexity and why every token boundary matters.

2026-02-19

Chapter 1 — What Is a Large Language Model? (Beyond the Headlines)

Chapter 1 of the LLM Primer I series. We unpack what 'Large,' 'Language,' and 'Model' actually mean, walk through the move from rule-based systems to neural networks, and address the three biggest misconceptions about how modern LLMs work. A clear, accessible foundation for everything that follows.

2026-02-18

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

2.1 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.

2025-09-08

Chapter 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.

2025-09-07

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

1.2 Basics of Probability for Language Generation

An intuitive, beginner-friendly guide to probability in Large Language Models. Learn how LLMs represent uncertainty, compute conditional probabilities, apply the chain rule, and generate text through sampling. This chapter builds the mathematical foundation for entropy and information theory in Section 1.3.

2025-09-05

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

Chapter 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.

2025-09-03

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

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