Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
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.
2026-04-12Chapter 12 — Protocol Hardening and Defenses
Thirteenth post of the LLM Primer IV walkthrough. The four defense clusters — cryptographic attestation, OAuth scope discipline with bounded sessions, runtime sandboxing, and human-in-the-loop gates — compose into a posture that does not depend on the model behaving correctly under adversarial conditions.
2026-04-10Chapter 9 — Managing the Attention Budget
Ninth post of the LLM Primer IV walkthrough. Context rot, the lost-in-the-middle cliff, tool-loadout rot, and the three architectural answers — MCP, RAG, fine-tuning — to the question of where a model's missing knowledge actually belongs.
2026-04-07Chapter 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.
2026-04-06Chapter 4 — Client Primitives: Agentic Behaviors and Control
Fourth post of the LLM Primer IV walkthrough. Sampling, Roots, and Elicitation are the three small, controlled holes MCP punches through the host-server wall — each a capability granted back, each a risk accepted on the user's behalf.
2026-04-02
Chapter 7 — Implementing Access Control
Seventh post of the LLM Primer III walkthrough. Document-level ACLs as the foundation, RBAC with Microsoft Purview sensitivity labels, ReBAC with Zanzibar and SpiceDB, and the pre-filter versus post-filter discipline that runs underneath all of them.
2026-03-24
Chapter 5 — Architecting the Retrieval Pipeline
Fifth post of the LLM Primer III walkthrough. Why a single vector search is not a pipeline — hybrid retrieval, reciprocal rank fusion, cross-encoder reranking, and query-side rewriting and HyDE — assembled into the production architecture that mature RAG systems converge on.
2026-03-22
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
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 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 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
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 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 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
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.
2026-02-17Part 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
6.1 Introducing Open-Source Tools and APIs
A preview from Chapter 6.1: Explore Hugging Face, OpenAI, Google Cloud Vertex AI, and Azure Cognitive Services—leading tools to bring LLMs into your projects.
2024-10-04
6.0 Hands-On with LLMs
A preview from Chapter 6: Learn how to run large language models yourself with open-source libraries, cloud APIs, and Python—making LLMs accessible to everyone.
2024-10-02
4.4 How LLMs Write Code: The Rise of AI-Powered Programming Assistants
Explore how large language models (LLMs) generate and complete code from natural-language prompts, and what it means for the future of software development.
2024-09-27
4.0 Applications of LLMs: Text Generation, Question Answering, Translation, and Code Generation
Discover how Large Language Models (LLMs) are used across various NLP tasks, including text generation, question answering, translation, and code generation. Learn about their practical applications and benefits.
2024-09-15
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
3.0 How to Train Large Language Models (LLMs): Data Preparation, Steps, and Fine-Tuning
Learn the key techniques for training Large Language Models (LLMs), including data preprocessing, forward and backward propagation, fine-tuning, and transfer learning. Optimize your model’s performance with efficient training methods.
2024-09-11
2.3 Key LLM Models: BERT, GPT, and T5 Explained
Discover the main differences between BERT, GPT, and T5 in the realm of Large Language Models (LLMs). Learn about their unique features, applications, and how they contribute to various NLP tasks.
2024-09-10
2.0 The Basics of Large Language Models (LLMs): Transformer Architecture and Key Models
Learn about the foundational elements of Large Language Models (LLMs), including the transformer architecture and attention mechanism. Explore key LLMs like BERT, GPT, and T5, and their applications in NLP.
2024-09-06
1.3 Differences Between Large Language Models (LLMs) and Traditional Machine Learning
Understand the key differences between Large Language Models (LLMs) and traditional machine learning models. Explore how LLMs utilize transformer architecture, offer scalability, and leverage transfer learning for versatile NLP tasks.
2024-09-05
1.1 Understanding Large Language Models (LLMs): Definition, Training, and Scalability Explained
Explore the fundamentals of Large Language Models (LLMs), including their structure, training techniques like pre-training and fine-tuning, and the importance of scalability. Discover how LLMs like GPT and BERT work to perform NLP tasks like text generation and translation.
2024-09-03
A Guide to LLMs (Large Language Models): Understanding the Foundations of Generative AI
Learn about large language models (LLMs), including GPT, BERT, and T5, their functionality, training processes, and practical applications in NLP. This guide provides insights for engineers interested in leveraging LLMs in various fields.
2024-09-01