Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 8 — Data Anonymization in the RAG Pipeline
Eighth post of the LLM Primer III walkthrough. Pre-generation versus post-generation anonymisation, the three technique families — masking, synthetic replacement, differential privacy — and the utility-privacy tradeoff that determines whether the system remains useful at all.
2026-03-25
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 12 — Building Your Own LLM System: From Datasets to Production
Chapter 12 of the LLM Primer I series. The final chapter. What it actually takes to build an LLM-powered system end to end — dataset licensing, training pipelines, evaluation frameworks, the integrated application stack, and the case-study patterns that distinguish successful deployments from failed pilots.
2026-03-01
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