Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
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 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 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 1 — The AI Integration Crisis and the Rise of Agentic Architecture
First post of the LLM Primer IV walkthrough. Why monolithic agents fray as system prompts grow, the N times M integration problem hiding underneath, and the move from prompt engineering to context engineering that MCP was built to enable.
2026-03-30
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
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
2.1 Transformer Model Explained: Core Architecture of Large Language Models (LLM)
Discover the Transformer model, the backbone of modern Large Language Models (LLM) like GPT and BERT. Learn about its efficient encoder-decoder architecture, self-attention mechanism, and how it revolutionized Natural Language Processing (NLP).
2024-09-07
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