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 42 articles available. | Currently on page 1 of 1.

Chapter 17 — Future Threats and Emerging Defenses

Seventeenth post of the LLM Primer VII walkthrough — and the series finale. Agent risks and the lethal trifecta, multimodal attack surfaces, deepfakes and C2PA provenance, plus a closing map of the whole LLM Primer arc and the Physical AI sister volume.

2026-05-26

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 13 — Regulatory Landscape

Thirteenth post of the LLM Primer VII walkthrough. The EU AI Act (Regulation 2024/1689), US EO 14179, Colorado AI Act, NIST AI RMF + GenAI Profile, and ISO/IEC 42001 as the compliance skeleton.

2026-05-22

Chapter 12 — Access Control and Identity

Twelfth post of the LLM Primer VII walkthrough. OAuth 2.0 + PKCE, ABAC vs ReBAC (Zanzibar), multi-tenant isolation, and token-bucket rate limits for LLM APIs.

2026-05-21

Chapter 11 — Observability, Logging, and Incident Response

Eleventh post of the LLM Primer VII walkthrough. Structured LLM logging with PII redaction, OpenTelemetry GenAI conventions, and the NIST SP 800-61 IR cycle adapted for probabilistic systems.

2026-05-20

Chapter 10 — Designing Secure LLM Architectures

Tenth post of the LLM Primer VII walkthrough. Isolation boundaries, policy engines (OPA, Cedar), microVM sandboxes, and the "lethal trifecta" of agent + private data + untrusted content.

2026-05-19

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

Chapter 3 — Data Security and Privacy

Third post of the LLM Primer VII walkthrough. Training-data risks, memorization and extraction (Carlini et al., Nasr et al.), and the encryption, isolation, and retention disciplines that keep sensitive prompts contained.

2026-05-12

Chapter 1 — Why AI Security Is Different

First post of the LLM Primer VII walkthrough. Why LLM security is structurally different from traditional security — the collapsed code/data boundary, the probabilistic core, and the OWASP LLM Top 10 as a working checklist.

2026-05-10

Chapter 5 — Demystifying Quantization

Fifth post of the LLM Primer VI walkthrough. From BF16 to INT4 to Blackwell FP4 — quantization algorithms (AWQ, GPTQ, GGUF, SmoothQuant), NVIDIA ModelOpt, and when quantization is safe versus lossy.

2026-04-27

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 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 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 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.

2026-04-10

Chapter 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.

2026-04-09

Chapter 10 — Long-Horizon Task Memory

Tenth post of the LLM Primer IV walkthrough. Short-term memory through windows and ReAct scratchpads, long-term memory through episodic vectors and semantic stores, and the compaction techniques that keep an agent productive over hours and days.

2026-04-08

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 7 — Advanced Collaborative and Dynamic Patterns

Seventh post of the LLM Primer IV walkthrough. Roundtable consensus, handoff routing, and magentic orchestration — the patterns that emerge when the topology has to be built per request, with the failure modes (non-termination, mis-routing, runaway planning) the simpler patterns avoid.

2026-04-05

Chapter 6 — Fundamental Orchestration Strategies

Sixth post of the LLM Primer IV walkthrough. The two foundational orchestration shapes — sequential pipelines and concurrent scatter-gather — and the prior question every team should ask: is a multi-agent system the right answer at all?

2026-04-04

Chapter 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 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 3 — Server Primitives: Exposing Context and Capabilities

Third post of the LLM Primer IV walkthrough. The three nouns an MCP server can offer — Resources (read state), Prompts (reusable scaffolding), Tools (write actions) — their schemas, their lifecycles, their error models, and the discipline of choosing the right primitive.

2026-04-01

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 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

LLM Primer IV — Series Introduction & Index

Kicking off the chapter-by-chapter walkthrough of Book IV in the LLM Primer series — Designing AI Cognition with MCP. Why agents need a protocol layer to scale past demoware, who this book is for, and the schedule for the fourteen posts that follow, March 30 through April 12.

2026-03-29

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 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 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 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 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 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 10 — Safety, Ethics, & Trust: Beyond the Marketing

Chapter 10 of the LLM Primer I series. The honest picture of LLM safety — why hallucinations happen mechanistically, where bias actually lives, how layered guardrails work, and why governance is the institutional layer that technical controls can't replace. For practitioners who need to ship safely.

2026-02-27

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 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

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