LLM

LangChain’s breakdown of the agent harness connects context engineering, memory, MCP, and the agent loop into one coherent map.

A long-form Prompt Engineering guide for AI application engineers, covering foundational principles, context design, task chains, injection defenses, agent prompt design, and evaluation-driven development.

A practical breakdown for AI engineers of what the M5’s changes over the M4—from CPU, cache, and memory bandwidth to Neural Accelerators—mean for local LLM and diffusion inference.

KV Cache is a key concept connecting Transformer theory with LLM engineering and deployment. Understanding it completes the path from how a model computes to how it runs.

Understand what LLM Chain-of-Thought (CoT) is and how prompt engineering can elicit Chain-of-Thought (CoT) from an LLM.

Prompt engineering? It may not be as simple as you think.

Understand LLM prompt injection and several fundamental defensive measures.

This article gave me—and may give you—a deeper understanding of the basic principles behind Transformers, replacing a black-box view of today’s mainstream LLMs. Cheers!