An intuitive introduction for programmers without a machine-learning background to gradient descent, loss functions, backpropagation, learning rates, and optimizers in LLM training, from next-token prediction through parameter updates.
Using the bandwidth gap between hidden states and tokens, this article offers one explanation for LLM engineering phenomena such as Chain of Thought, prompt length, few-shot learning, personality drift, and hallucinations.
A systematic tour of the entire LLM training pipeline—from data pipelines, scaling laws, system constraints, synthetic data, distillation, post-training, and evaluation systems to agent training—and how these mechanisms affect model selection, evaluation, and harness design for AI application engineers.
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.
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!