Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 14 — Bias, Fairness, and Responsible AI
Fourteenth post of the LLM Primer VII walkthrough. Sources of bias in LLMs, measurement (BBQ, BOLD, StereoSet, HELM), and the safety-utility trade-off honestly named.
2026-05-23
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 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 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
LLM Primer VI — Series Introduction & Index
Kicking off the chapter-by-chapter walkthrough of Book VI in the LLM Primer series — Scaling AI Systems. Why inference is the discipline that decides whether an LLM app survives real users, and the schedule for the sixteen posts that follow, April 23 through May 8.
2026-04-22
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 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 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
7.4 Data Ethics and Bias in Large Language Models
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.
2024-10-09
7.3 Integrating Multimodal Models
A preview from Chapter 7.3: Discover how multimodal models fuse text, images, audio, and video to unlock richer AI capabilities beyond text-only LLMs.
2024-10-09
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
5.2 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
4.3 LLMs in Translation and Summarization: Enhancing Multilingual Communication
Learn how Large Language Models (LLMs) leverage Transformer architectures for accurate translation and summarization, improving efficiency in business, media, and education.
2024-09-18
2.3 Key LLM Models: BERT, GPT, and T5 Explained
Discover the main differences between BERT, GPT, and T5 in the realm of Large Language Models (LLMs). Learn about their unique features, applications, and how they contribute to various NLP tasks.
2024-09-10
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
1.1 Understanding Large Language Models (LLMs): Definition, Training, and Scalability Explained
Explore the fundamentals of Large Language Models (LLMs), including their structure, training techniques like pre-training and fine-tuning, and the importance of scalability. Discover how LLMs like GPT and BERT work to perform NLP tasks like text generation and translation.
2024-09-03
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