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 15 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 — Cost-Cutting Strategies in Production

Sixteenth and final post of the LLM Primer VI walkthrough. Intelligent model routing, context compaction, async batch APIs, and semantic caching — plus a look ahead to Volume VII on AI Security.

2026-05-08

Chapter 2 — The KV Cache Challenge

Second post of the LLM Primer VI walkthrough. The KV cache formula, the attention-variant trade-offs (MHA vs GQA vs MQA), and the memory-fragmentation problem PagedAttention solves.

2026-04-24

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 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 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 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 6 — Transformer Blocks and Representation Power

Sixth post of the LLM Primer II walkthrough. Feed-forward layers, activation functions, why "attention + FFN" is exactly the right pair, and what mathematical guarantees depth and width give you about expressivity.

2026-03-08

Chapter 4 — Attention: The Core Mechanism

Fourth post of the LLM Primer II walkthrough. Self-attention derived from intuition, the geometry of queries/keys/values, multi-head structure and normalization, softmax in detail with its temperature knob, and a striking final move: attention seen as a kernel method.

2026-03-06

Chapter 8 — Using LLMs in Applications: Chatbots, Code, Extraction, and Agents

Chapter 8 of the LLM Primer I series. The application patterns that actually ship in production — chatbots, summarization, code assistants, structured extraction, and the rise of agentic systems where the model drives a tool-use loop. Plus the benchmarks every engineer should recognize by name.

2026-02-25

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

Chapter 3 — Neural Networks for Language: From RNNs to Self-Attention

Chapter 3 of the LLM Primer I series. Why feedforward networks couldn't handle language, how RNNs hit a wall, and what attention changed. A clean conceptual progression through the three neural-network shapes that defined modern NLP — without the math anxiety.

2026-02-20