Master the theory and implementation of RAG (Retrieval-Augmented Generation) from fundamentals to production deployment
Series Overview
This series is an intermediate-advanced educational content consisting of 4 chapters, designed to help you learn the knowledge required to build RAG systems with implementation code.
Features:
- ✅ Implementation-First: Practical learning with over 21 working code examples
- ✅ Progressive Learning: Systematic coverage from fundamentals to production operation
- ✅ Latest Technologies: Utilizing OpenAI, Langchain, FAISS, Chroma, Pinecone, and more
- ✅ Practical Applications: From chunking strategies to production optimization
Chapter Details
Chapter 1: RAG Fundamentals
Difficulty: Intermediate | Learning Time: 30-35 minutes | Code Examples: 6
Learning Content
- What is RAG - Architecture and operating principles
- Document processing - Loaders and parsers
- Chunking strategies - Fixed-length, sentence boundary, semantic
- Metadata management - Filtering and improving search accuracy
- Practice: Building a basic RAG pipeline
Chapter 2: Embeddings and Search
Difficulty: Intermediate | Learning Time: 30-35 minutes | Code Examples: 6
Learning Content
- Vector embeddings - Semantic representation of text
- Similarity search - Cosine similarity, Euclidean distance
- FAISS - High-speed similarity search engine
- Chroma - Vector database implementation
- Pinecone - Cloud-based vector database
- Practice: Search implementation with each vector database
Chapter 3: Advanced RAG Techniques
Difficulty: Advanced | Learning Time: 30-35 minutes | Code Examples: 5
Learning Content
- Query optimization - Query Decomposition, HyDE
- Reranking - Cross-Encoder, MMR algorithm
- Hybrid search - Fusion of keyword and vector search
- Context compression - Token reduction and quality improvement
- Practice: Building advanced search pipelines
Chapter 4: Production Deployment
Difficulty: Advanced | Learning Time: 30-40 minutes | Code Examples: 6
Learning Content
- System architecture - Microservices design
- Performance optimization - Caching, batch processing
- Monitoring and evaluation - Metrics design, A/B testing
- Scalability - Distributed processing, load balancing
- Security - Access control, data privacy
- Practice: Building production RAG systems
Prerequisites
Required (Must Have)
- ✅ Intermediate Python - Understanding of classes and asynchronous processing
- ✅ Machine learning fundamentals - Concepts of embeddings and similarity
- ✅ Introduction to NLP - Tokenization and text preprocessing
Recommended (Nice to Have)
- 🔵 LLM fundamentals - GPT, prompt engineering
- 🔵 Database fundamentals - SQL, NoSQL
- 🔵 Web API development - REST, FastAPI
Technologies Used
- OpenAI API - GPT-4, Embeddings API
- Langchain 0.1+ - RAG framework
- FAISS - Vector similarity search
- ChromaDB - Vector database
- Pinecone - Cloud vector database
- Sentence-Transformers - Embedding generation
Learning Pathway
- Chapter 1: Understand the basic concepts of RAG and document processing
- Chapter 2: Master vector embeddings and search technologies
- Chapter 3: Learn advanced techniques to improve search accuracy
- Chapter 4: Practice building and operating systems in production environments
Update History
- 2025-10-25: v1.0 initial release
Disclaimer
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