Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
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 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 9 — Model Integrity and Supply Chain Risks
Ninth post of the LLM Primer VII walkthrough. Open-source model dependency risk, Sleeper Agents (Hubinger et al.), safetensors vs pickle, CVE-2024-3568, and the SLSA / Sigstore artifact-signing discipline.
2026-05-18
Chapter 8 — Adversarial Attacks on Models
Eighth post of the LLM Primer VII walkthrough. Adversarial examples in NLP (HotFlip, TextFooler), model extraction (Tramèr et al., Carlini et al.), and the defensive strategies for API-boundary abuse.
2026-05-17
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 6 — Retrieval-Augmented Generation Risks
Sixth post of the LLM Primer VII walkthrough. Trust boundaries in RAG, malicious document injection, PoisonedRAG and BadRAG, and monitoring retrieval flows for the attacker's fingerprints.
2026-05-15
Chapter 5 — Input Validation and Output Filtering
Fifth post of the LLM Primer VII walkthrough. Input sanitization, structured guardrails (NeMo, Llama Guard 3, Lakera, Bedrock), and red teaming with Garak, PyRIT, and promptfoo.
2026-05-14
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
LLM Primer VII — Series Introduction & Index
Kicking off the chapter-by-chapter walkthrough of Book VII in the LLM Primer series — AI Security. Why in LLM systems code and data are the same string, and the schedule for the seventeen posts that follow, May 10 through May 26. This is the series finale.
2026-05-09
Chapter 15 — Serverless APIs vs Dedicated Infrastructure
Fifteenth post of the LLM Primer VI walkthrough. The breakeven math between serverless APIs and dedicated infrastructure, the hidden platform-engineering overhead each side takes on, and microVM sandboxes for agent code execution.
2026-05-07
Chapter 14 — Token Economics and API Pricing
Fourteenth post of the LLM Primer VI walkthrough. The input-vs-output token asymmetry, the hidden cost of conversation history, and the invisible reasoning tokens that quietly rewrite the daily bill.
2026-05-06
Chapter 11 — The Platform and Orchestration Layer
Eleventh post of the LLM Primer VI walkthrough. Engine vs platform — Ray Serve, KServe, BentoML, and NVIDIA Triton — and where each fits in a multi-model pipeline.
2026-05-03
Chapter 10 — The LLM Engine Layer
Tenth post of the LLM Primer VI walkthrough. vLLM as the safe default, TensorRT-LLM for peak NVIDIA-only throughput, SGLang for structured and agentic outputs, and TGI/Ollama for the rest.
2026-05-02
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
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 4 — Specialized AI Silicon and ASICs
Fourth post of the LLM Primer VI walkthrough. Groq LPUs, AWS Inferentia2, Google TPUs, and Intel Gaudi — where specialized silicon fits alongside general-purpose GPUs.
2026-04-26
Chapter 3 — Data Center GPUs for Generative AI
Third post of the LLM Primer VI walkthrough. The NVIDIA lineup (H100, H200, B200, L40S) vs AMD MI300X — and why HBM bandwidth matters more than FLOPs for decoding.
2026-04-25
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 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 13 — Frameworks and Cloud Integration
Fourteenth post of the LLM Primer IV walkthrough. Strands with Bedrock, the AWS state-layer pattern, the Microsoft Agent Framework, LangChain, Semantic Kernel — and the three production integration shapes teams keep arriving at independently.
2026-04-11
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 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.
2026-04-07
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 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 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 11 — Continuous Updates and Pipeline Optimization
Eleventh and final post of the LLM Primer III walkthrough. CDC and incremental indexing keep the corpus fresh, semantic caching and model tiering keep latency down, and a four-stage feedback loop closes the gap between what production tells the team and what the team actually changes — plus a bridge to Volume IV on Model Context Protocol.
2026-03-28
Chapter 10 — Leading Evaluation Frameworks
Tenth post of the LLM Primer III walkthrough. A field guide to the frameworks that turn the Evaluation Triad into something a team can actually run — RAGAS, TruLens, DeepEval on one side, Braintrust, LangSmith, Phoenix, Galileo, Opik on the other, and the Evaluation Gap none of them has yet closed.
2026-03-27
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 6 — RAG Threat Models and Vulnerabilities
Sixth post of the LLM Primer III walkthrough. The expanded attack surface of retrieval — corpus poisoning, adversarial chunks, indirect prompt injection, embedding inversion, and the confused-deputy problem in agentic RAG. Concrete attacks, each demonstrated, each reproducible.
2026-03-23
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 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 3 — Advanced Chunking Frameworks
Third post of the LLM Primer III walkthrough. The chunking spectrum from fixed-size to structure-aware, the overlap myth, the context cliff that destroys retrieval quietly, and the contextual-retrieval and late-chunking techniques that have reshaped the frontier.
2026-03-20
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 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 14 — Practical Knowledge for Engineers
Twelfth post — the closing chapter of the LLM Primer II walkthrough. How to keep deepening your understanding after the book ends, the tools and libraries that turn the math into shipping work, and the bridge to the other books in the LLM Primer series.
2026-03-16