Hands-On Course on Generative AI and RAG Technology: A Complete Learning Path from BERT to LangChain

Course Basic Information

• Original Author/Maintainer: RK0297
• Source Platform: GitHub
• Original Project Title: Generative-AI-and-RAG-Coursework
• Original Link: https://github.com/RK0297/Generative-AI-and-RAG-Coursework
• Release Date: June 6, 2026

Core Overview

This course offers a full-stack technical learning path from BERT natural language processing fundamentals to ChromaDB vector search, LangChain framework integration, and Kafka/PySpark real-time data stream processing. It deeply解析 the core concepts, implementation principles, and application scenarios of each module, providing a structured guide for engineers to systematically master large model application development.

With the explosion of large language models like ChatGPT, Generative AI has become a technical hotspot, but developers generally lack a systematic grasp of the complete tech stack from traditional NLP to modern RAG applications. This course fills this gap by adopting a full-stack design philosophy, covering underlying NLP principles, vector databases, application frameworks, and real-time data processing links, helping learners understand the working principles of RAG systems rather than just calling ready-made APIs.

Module 1: BERT and NLP Basics

• Core Innovation of BERT: Bidirectional encoder design, enabling bidirectional context understanding through Masked Language Model (MLM)
• Application Scenarios: Downstream tasks such as text classification, named entity recognition, and question-answering systems
• Key Practices: Distinguish between fine-tuning (for data-sufficient scenarios) and feature extraction (for data-scarce scenarios)

Module 2: Vector Search and ChromaDB

• Vector Embedding Principle: Map unstructured data to vector space, where semantically similar content is close in distance
• Advantages of ChromaDB: Optimized for AI, providing concise APIs and efficient performance
• Core Operations: Generate text embeddings, manage vector collections, perform similarity search and MMR retrieval, configure distance measurement methods

Understanding vector search is key to building efficient RAG systems; retrieval quality directly affects the relevance and accuracy of generated content.

LangChain is a popular LLM application development framework with core abstractions including:

• Chains: Combine multiple components to form reusable workflows (prompt templates + models/multi-step reasoning, etc.)
• Retrievers: Encapsulate vector search logic, supporting multiple vector databases (ChromaDB/Pinecone, etc.)
• Memory: Store historical conversation content to enable multi-turn dialogue context understanding
• Agents: Allow models to autonomously decide to call external tools through the ReAct framework

The course demonstrates building question-answering robots, document summarization tools, and other applications through cases, emphasizing the importance of prompt engineering.

Role of Kafka

• Document Ingestion Pipeline: Deliver new documents to processing clusters for embedding computation and index updates
• Query Log Collection: Record user query history for retrieval quality analysis and model optimization
• Event-Driven Architecture: Achieve component decoupling communication to improve system scalability

Application of PySpark

• Batch Document Processing: Parallelize embedding computation for large document collections
• Data Cleaning and Transformation: Process raw content from heterogeneous data sources and extract structured information
• Feature Engineering: Compute document statistical features for retrieval ranking and result filtering

The course guides setting up local Kafka/Spark clusters, writing producer/consumer programs, and designing scalable data flow topologies.

It is recommended to proceed step by step in the following stages:

1. Solidify Foundations: Understand Transformer architecture and BERT principles, conduct fine-tuning experiments, and derive attention weight calculations
2. Hands-On Practice: Set up a local ChromaDB instance, import personal documents to build a knowledge retrieval system, and compare the effects of different embedding models
3. System Integration: Use LangChain to integrate vector retrieval and LLM, build a complete RAG application, and design retrieval failure fallback strategies
4. Production Optimization: Learn Kafka/PySpark to build data pipelines, understand distributed system concepts, and deploy applications to cloud platforms

Skipping basics to directly learn advanced content may lead to comprehension difficulties.

Summary

This course provides a structured framework for learning Generative AI and RAG technology, covering the tech stack required for production-level AI applications, and is a hands-on guide to systematically mastering large model development. Although technology iterates rapidly, core concepts (vector representation, RAG, stream processing) have long-term value.

Advanced Directions

• Multimodal RAG: Extend retrieval scope to images/audio/videos (based on cross-modal models like CLIP)
• Agent Systems: Learn AutoGPT/LangGraph to build autonomous planning intelligent agents
• Model Fine-Tuning: Master parameter-efficient fine-tuning techniques such as LoRA/QLoRA
• Evaluation and Optimization: Use the RAGAS framework to measure retrieval and generation quality, and optimize system bottlenecks in a targeted manner