Introduction to LLMs for Developers
This course is a Chinese-adapted version of the three-course large language model series jointly launched by Andrew Ng and OpenAI. It covers Prompt Engineering, building systems with the ChatGPT API, LangChain application development, and using LangChain to access private data. The course provides a clear and accessible introduction to how to leverage the capabilities of large language models to build applications with summarization, inference, transformation, expansion, and chat functions.
Lessons
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.
Course Overview
📚 Content Summary
This course is a Chinese adaptation of the three-part Large Language Model (LLM) series co-developed by Andrew Ng and OpenAI. It covers Prompt Engineering, building systems based on the ChatGPT API, LangChain application development, and using LangChain to access private data. The course provides an in-depth yet accessible introduction to leveraging LLM capabilities to develop applications featuring summarization, inference, transformation, expansion, and chat functionalities.
Master Prompt Engineering and the LangChain framework to become an AI application developer in the era of large models.
🎯 Learning Objectives
- Distinguish between Base LLMs and Instruction Tuned LLMs.
- Apply four specific tactics to write clear and specific instructions (delimiters, structured output, condition checks, and few-shot prompting).
- Implement strategies to give models "time to think" by specifying task steps and requiring independent problem-solving.
- Implement Structured Text Processing: Summarize and extract specific information from single or multiple documents while controlling output length and focus.
- Perform Automated Text Analysis: Categorize sentiment, identify specific emotions, and execute zero-shot topic classification using prompts.
- Execute Multi-modal Transformations: Translate between languages, convert data formats (e.g., JSON to HTML), and programmatically correct grammar/spelling.
- Distinguish between Base LLMs and Instruction-Tuned LLMs while understanding the impact of tokenization on model behavior.
- Implement structured system architectures using System, User, and Assistant message roles to define model personas.
- Apply classification and moderation techniques to evaluate user inputs for safety and routing.
- Implement Prompt Chaining: Decompose complex tasks into manageable sub-tasks to improve reliability and reduce costs.
Lessons
Overview: This lesson covers the fundamental principles of prompt engineering for developers, transitioning from basic "Base LLMs" to "Instruction Tuned LLMs." It introduces two primary pillars for effective interaction: writing clear, specific instructions and allowing the model sufficient "time to think." Furthermore, it establishes an iterative workflow for refining prompts to meet specific business requirements, such as length constraints and formatting needs.
Learning Outcomes:
- Distinguish between Base LLMs and Instruction Tuned LLMs.
- Apply four specific tactics to write clear and specific instructions (delimiters, structured output, condition checks, and few-shot prompting).
- Implement strategies to give models "time to think" by specifying task steps and requiring independent problem-solving.
Overview: This lesson explores the practical application of Large Language Models (LLMs) across five key functional areas: Summarizing, Inferring, Transforming, Expanding, and building interactive Chatbots. It provides developers with a hands-on guide to using Python and the OpenAI API to process text, extract structured data, adjust linguistic styles, and maintain conversational context for real-world applications like customer service bots.
Learning Outcomes:
- Implement Structured Text Processing: Summarize and extract specific information from single or multiple documents while controlling output length and focus.
- Perform Automated Text Analysis: Categorize sentiment, identify specific emotions, and execute zero-shot topic classification using prompts.
- Execute Multi-modal Transformations: Translate between languages, convert data formats (e.g., JSON to HTML), and programmatically correct grammar/spelling.
Overview: This lesson explores the fundamental mechanics of Large Language Models (LLMs), moving from basic tokenization and message roles to advanced input handling strategies. Students will learn how to build robust systems by classifying user intent, moderating content for safety, and implementing Chain of Thought (CoT) reasoning to improve accuracy and control the user experience through "Inner Monologues."
Learning Outcomes:
- Distinguish between Base LLMs and Instruction-Tuned LLMs while understanding the impact of tokenization on model behavior.
- Implement structured system architectures using System, User, and Assistant message roles to define model personas.
- Apply classification and moderation techniques to evaluate user inputs for safety and routing.
Overview: This lesson covers the transition from simple prompts to complex, multi-step LLM workflows. It focuses on "Prompt Chaining" to decompose tasks, implementing safety layers via the Moderation API, and establishing rigorous evaluation frameworks—both for cases with a single correct answer and those requiring subjective, rubric-based assessment.
Learning Outcomes:
- Implement Prompt Chaining: Decompose complex tasks into manageable sub-tasks to improve reliability and reduce costs.
- Build a Secure End-to-End System: Integrate input/output moderation and fact-checking mechanisms into a production-ready pipeline.
- Develop Evaluation Strategies: Create validation sets to measure LLM performance using "ideal" answer comparisons and LLM-assisted rubrics.
Overview: This lesson introduces the core components of the LangChain framework, specifically focusing on how to abstract Large Language Model (LLM) interactions. It covers the transition from direct API calls to using LangChain's modular components: Models, Prompt Templates, and Output Parsers, while concluding with a deep dive into four distinct Memory strategies for maintaining conversation state.
Learning Outcomes:
- Implement basic LLM calls using LangChain's ChatOpenAI and modular components.
- Construct reusable ChatPromptTemplate structures to manage complex prompt logic and variables.
- Extract structured data (JSON/Python Dictionaries) from unstructured LLM responses using StructuredOutputParser.
Overview: This lesson covers the core architectural building blocks of LangChain, transitioning from simple linear prompt-completion sequences to complex, logic-driven systems. Students will learn how to orchestrate multiple LLM calls through Chains, implement Retrieval Augmented Generation (Q&A) over private documents using vector stores, and deploy Autonomous Agents that use reasoning to select and execute tools. The material emphasizes moving beyond single-prompt interactions toward automated, scalable LLM applications.
Learning Outcomes:
- Construct linear and complex multi-step workflows using SequentialChain and RouterChain.
- Build a document-based Q&A system using embeddings, vector stores, and the RetrievalQA chain.
- Implement automated evaluation of LLM applications using QAGenerateChain and QAEvalChain.
Overview: This lesson covers the initial stages of building a Retrieval Augmented Generation (RAG) system using LangChain. It guides developers through loading unstructured data from diverse sources (PDFs, YouTube, Web, Notion), splitting that data into semantically meaningful chunks, and transforming those chunks into numerical embeddings for storage and similarity search in vector databases.
Learning Outcomes:
- Implement various LangChain Document Loaders to import data from PDFs, YouTube audio, web URLs, and Notion databases.
- Configure and compare different Text Splitters (Character, Recursive, Token, and Markdown) using parameters like chunk_size and chunk_overlap.
- Execute the Vectorization process by creating embeddings and storing them in a Chroma vectorstore to enable semantic similarity searches.
Overview: This material explores the transition from basic semantic search to advanced retrieval and conversational interaction within LLM applications. It covers sophisticated retrieval techniques—such as Maximum Marginal Relevance (MMR), metadata filtering, and contextual compression—before demonstrating how to build stateful Q&A chains and full-featured chatbots using LangChain's memory and GUI components.
Learning Outcomes:
- Implement advanced retrieval strategies (MMR, Self-Querying, and Compression) to improve the diversity and relevance of retrieved documents.
- Compare and deploy different chain types (stuff, MapReduce, Refine) for processing retrieved context in question-answering tasks.
- Construct a stateful chatbot interface using ConversationalRetrievalChain and ConversationBufferMemory to maintain dialogue context.