A Machine Learning Engineer’s Guide to LLM Concepts: Tokens, Transformers, Embeddings, Prompts, RAG, and Fine-Tuning
English summary
This tutorial provides a practical overview of core LLM concepts for machine learning engineers. It begins with foundational elements like tokens, transformer architectures, and embeddings, then covers advanced techniques including prompt engineering, retrieval-augmented generation (RAG), and fine-tuning. The guide emphasizes developing sound engineering judgment to move beyond trial-and-error prompting. No new research or product announcements are made; it serves as an educational resource.
Chinese summary
本教程为机器学习工程师提供了LLM核心概念的实用概述。从标记、Transformer架构和嵌入等基础元素开始，然后涵盖提示工程、检索增强生成（RAG）和微调等高级技术。该指南强调培养合理的工程判断力，以超越试错式的盲目提示。未涉及新研究或产品发布，仅作为教育资源。
Key points
Covers tokenization, transformer models, and embeddings as foundational LLM concepts.
涵盖分词、Transformer模型和嵌入作为LLM基础概念。
Explains prompt engineering, retrieval-augmented generation (RAG), and fine-tuning as practical techniques.
讲解提示工程、检索增强生成（RAG）和微调等实用技术。
Aims to develop reliable engineering judgment for working with LLMs.
旨在培养使用LLM时可靠的工程判断力。
Written as a field guide for machine learning engineers to move beyond blind prompting.
作为机器学习工程师的实地指南，避免盲目提示。