Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 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 11 — Attack Surfaces and Protocol Vulnerabilities
Eleventh post of the LLM Primer IV walkthrough. The classical attacks adapted to MCP — Confused Deputy, Token Passthrough, Session Hijacking — the protocol-level flaws around capability escalation and unauthenticated sampling, and the implicit trust propagation that makes context poisoning a structural problem rather than a hygiene one.
2026-04-09Chapter 5 — Transport Protocols and Discovery
Fifth post of the LLM Primer IV walkthrough. The three transports MCP supports, the .well-known discovery layer with Server Cards, and the boring operational concerns — CORS, origin validation, caching — that decide whether a server is a cooperative network citizen or a liability.
2026-04-03
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 2 — Intelligent Document Parsing
Second post of the LLM Primer III walkthrough. Why a PDF is not a text file, what layout-aware parsers actually preserve, the current tool landscape (LlamaParse, Docling, Unstructured, Marker-PDF, Firecrawl, DeepSeek-OCR), and the multimodal track that retrieves over page images directly.
2026-03-19
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.
2026-03-042.1 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.
2025-09-07Understanding 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
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
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