Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
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 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 — 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
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 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 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 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 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 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 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 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
The LLM Primer Series — A Field Guide to Generative AI, Built One Volume at a Time
The LLM Primer Series — a completed seven-volume field guide to generative AI by Sho Shimoda. From foundations to security. Includes Physical AI as sister volume. All 7 volumes available on Amazon.
2026-02-15
7.0 Future Outlook and Challenges
A preview from Chapter 7: Explore the future of large language models—ethics, efficiency, multimodal AI, and responsible governance beyond scaling.
2024-10-06
6.2 Simple Python Experiments with LLMs
A preview from Chapter 6.2: Learn how to run large language models with Hugging Face, OpenAI, Google Cloud, and Azure using just Python and a few lines of code.
2024-10-05
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
5.3 Real-Time Deployment Challenges
A preview from Chapter 5.3: Explore latency, scalability, and optimization techniques for deploying large language models in real-time applications.
2024-10-01
5.2 Compute Resources and Cost
A preview from Chapter 5.2: Learn why LLMs demand massive compute power, what drives cost, and practical strategies to optimize performance and sustainability.
2024-09-30
5.1 Bias & Ethical Considerations
A preview from Chapter 5.1 of our book: uncover how large language models inherit bias and learn strategies to build fair, trustworthy AI.
2024-09-29
4.2 Enhancing Customer Support with LLM-Based Question Answering Systems
Discover how Question Answering Systems powered by Large Language Models (LLMs) are transforming customer support, search engines, and specialized fields with high accuracy and flexibility.
2024-09-17
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