開發者入門：大型語言模型
本課程是安德魯·吳與 OpenAI 聯合推出的三門大型語言模型系列課程的中文適配版本。內容涵蓋提示工程、使用 ChatGPT API 建立系統、LangChain 應用開發，以及利用 LangChain 存取私有資料。課程為大規模語言模型的應用能力提供了清晰且易於理解的介紹，幫助學習者掌握如何建構具備摘要、推理、轉換、擴展及對話功能的應用程式。
課程總覽
📚 內容摘要
本課程是安德魯·吳（Andrew Ng）與 OpenAI 聯合開發的三部分大型語言模型（LLM）系列課程的中文版本。內容涵蓋提示工程、基於 ChatGPT API 建立系統、LangChain 應用開發，以及使用 LangChain 存取私有資料。課程提供深入且易懂的介紹，幫助學習者掌握如何運用 LLM 的能力，開發具備摘要、推論、轉換、擴展及對話功能的應用。
精通提示工程與 LangChain 框架，成為大模型時代的 AI 應用開發者。
🎯 學習目標
- 区分基礎型 LLM 與指令微調型 LLM。
- 運用四種特定技巧撰寫清晰且明確的指令（分隔符、結構化輸出、條件檢查、少樣本提示）。
- 實施策略讓模型「有時間思考」，透過指定任務步驟並要求獨立解決問題。
- 實作結構化文字處理：從單一或多份文件中總結並提取特定資訊，同時控制輸出長度與重點。
- 進行自動化文字分析：以提示語句分類情感、識別特定情緒，並執行零樣本主題分類。
- 執行多模態轉換：跨語言翻譯、轉換資料格式（例如：JSON 轉 HTML），並程式化修正語法/拼字錯誤。
- 辨別基礎型 LLM 與指令微調型 LLM，並理解詞彙化（tokenization）對模型行為的影響。
- 使用 System、User 與 Assistant 消息角色建立結構化系統架構，定義模型人格。
- 應用分類與審查技術，評估使用者輸入的安全性與路由。
- 實施提示鏈（Prompt Chaining）：將複雜任務分解為可管理的子任務，提升可靠性並降低成本。
課程 共 8 课时 · 预计 24.0h
課程
Lesson
This lesson introduces the shift from base LLMs to instruction-tuned models, emphasizing the importance of clear, specific prompting and iterative refinement to achieve reliable results. Developers will learn to treat models as literal assistants by using techniques like delimiters, chain-of-thought reasoning, and structured output to improve accuracy and minimize hallucinations.
This lesson introduces the five core prompting primitives—summarizing, inferring, transforming, expanding, and chatbots—that enable developers to build structured AI-driven workflows. You will learn how to manage LLM state, use delimiters for precision, and choose the right techniques to extract data or analyze sentiment effectively.
This lesson explores how LLMs process information through numerical tokens rather than individual characters, explaining why techniques like hyphenation are necessary for tasks requiring granular detail. It also distinguishes between base models, which focus on pattern prediction, and instruction-tuned models, which are optimized through human feedback to act as reliable assistants.
This lesson introduces prompt chaining and task decomposition as superior alternatives to complex, single-prompt workflows. You will learn how to build modular, cost-effective pipelines that improve reliability and debuggability by integrating dynamic data retrieval, safety moderation, and step-by-step logic.
This lesson introduces LangChain as a modular framework for managing LLM interactions, highlighting how abstractions like ChatOpenAI and ChatPromptTemplate help developers overcome the stateless nature of models. You will learn to manage conversation history, configure temperature for deterministic or creative outputs, and use templates to separate logic from content for scalable application development.
This lesson introduces LangChain orchestration, teaching you how to move beyond single prompts by linking components into sequential pipelines and logic-driven router chains. You will learn to build scalable AI applications by mastering modular workflows that connect models, prompts, and external data to handle complex, multi-step tasks.
This lesson explores the Retrieval-Augmented Generation (RAG) pipeline, focusing on how to ingest, split, and vectorize private data to provide LLMs with relevant, up-to-date context. You will learn to use document loaders to standardize various file formats into structured objects and apply effective chunking strategies to ensure semantic meaning is preserved for accurate retrieval.
This lesson explores advanced Retrieval-Augmented Generation (RAG) techniques designed to overcome the limitations of basic semantic search, such as redundancy and context window constraints. You will learn how to implement tools like Maximum Marginal Relevance (MMR), self-querying, and contextual compression to provide the LLM with a more diverse, filtered, and efficient knowledge base.