RAG Systems Complete
Build Intelligent Document Q&A Systems
Course Code: RAG-301
Duration: 10 Weeks
Level: Intermediate to Advanced
Instructor: inskillflow Academy
Course Overview
This comprehensive course will teach you everything you need to know about building production-ready Retrieval-Augmented Generation (RAG) systems. From understanding the fundamentals to deploying enterprise-scale applications, you'll gain hands-on experience with the latest frameworks and best practices.
Learning Objectives
By the end of this course, you will be able to:
- Understand RAG architecture and why it solves LLM limitations
- Process and chunk documents for optimal retrieval
- Work with embeddings and vector databases
- Implement advanced retrieval strategies
- Build complete RAG pipelines with LangChain and LlamaIndex
- Evaluate and optimize RAG systems
- Deploy production-ready RAG applications
- Build real-world projects from scratch
Prerequisites
- Intermediate Python programming skills
- Basic understanding of APIs and REST
- Familiarity with machine learning concepts
- Access to OpenAI API or similar LLM provider
Teaching Methodology
70% Hands-on Practice - 30% Theory
Each module combines:
- Conceptual explanations with diagrams
- Complete code examples
- Practical exercises
- Real-world case studies
Course Structure - 10 Modules
Module 1 - Introduction to RAG
Duration: Week 1
Content:
- What is RAG?
- Why RAG? Problems with Pure LLMs
- RAG Architecture Overview
- End-to-end flow diagrams
Objectives:
- Understand the RAG paradigm
- Learn why retrieval augments generation
- Visualize the complete RAG pipeline
Module 2 - Document Processing
Duration: Week 2
Content:
- Document Loaders (LangChain, LlamaIndex)
- Text Splitting Strategies
- Handling Different File Types
- OCR and structured data
Practical Exercises:
- Exercise 1: Load PDFs, Word, HTML documents
- Exercise 2: Implement different chunking strategies
Module 3 - Embeddings Deep Dive
Duration: Week 3
Content:
- What are Embeddings?
- Embedding Models (OpenAI, Cohere, Open Source)
- Choosing the Right Embedding Model
- Benchmarks and performance analysis
Practical Exercises:
- Exercise 1: Generate and visualize embeddings
- Exercise 2: Compare different embedding models
Module 4 - Vector Databases
Duration: Week 4
Content:
- Introduction to Vector Stores
- Pinecone, Weaviate, Chroma
- Milvus, Qdrant, pgvector
- Choosing the right database
Practical Exercises:
- Exercise 1: Set up multiple vector databases
- Exercise 2: Performance benchmarking
Module 5 - Retrieval Strategies
Duration: Week 5
Content:
- Similarity Search
- Hybrid Search (BM25 + Semantic)
- Reranking and Filtering
- Post-retrieval processing
Practical Exercises:
- Exercise 1: Implement different search strategies
- Exercise 2: Build a reranking pipeline
Module 6 - Building RAG Pipelines
Duration: Week 6
Content:
- LangChain for RAG
- LlamaIndex Framework
- Custom RAG Implementations
- Comparison of approaches
Practical Exercises:
- Exercise 1: Build a complete LangChain RAG pipeline
- Exercise 2: Build a LlamaIndex query engine
Module 7 - Advanced RAG Techniques
Duration: Week 7
Content:
- Query Transformation
- Multi-Query and Fusion
- Self-Query and Metadata Filtering
- HyDE (Hypothetical Document Embeddings)
Practical Exercises:
- Exercise 1: Implement query transformation
- Exercise 2: Build a multi-query RAG system
Module 8 - Evaluation and Optimization
Duration: Week 8
Content:
- RAG Evaluation Metrics
- RAGAS Framework
- Debugging RAG Pipelines
- Performance optimization
Practical Exercises:
- Exercise 1: Evaluate a RAG system with RAGAS
- Exercise 2: Debug and optimize a pipeline
Module 9 - Production RAG Systems
Duration: Week 9
Content:
- Scaling RAG Applications
- Caching and Performance
- Security and Access Control
- Multi-tenancy
Practical Exercises:
- Exercise 1: Implement semantic caching
- Exercise 2: Build a multi-tenant system
Module 10 - Real-World Projects
Duration: Week 10
Content:
- Building a Document Q&A System
- Enterprise Knowledge Base
- Multi-Tenant RAG Applications
- Deployment strategies
Final Project:
- Complete RAG application from scratch
- Presentation and documentation
Tech Stack
Frameworks
- LangChain: Chains, retrievers, LCEL
- LlamaIndex: Index types, query engines
- RAGAS: Evaluation framework
Vector Databases
- Pinecone (Hosted)
- Weaviate (Open Source)
- Chroma (Local development)
- Qdrant (Production)
- PostgreSQL + pgvector
Embedding Models
- OpenAI text-embedding-3
- Cohere embed-v3
- Sentence Transformers
- BGE models
Assessment Structure
Module Quizzes (40%)
- 30 questions per module
- Multiple choice format
- Tests conceptual understanding
Practical Assignments (30%)
- Assignment 1: Document processing pipeline (10%)
- Assignment 2: RAG pipeline implementation (10%)
- Assignment 3: Advanced retrieval system (10%)
Final Project (30%)
- Complete RAG application (20%)
- Presentation and documentation (10%)
Total: 100%
Course Progress
| Status | Module | Link |
|---|---|---|
| ✅ Complete | Module 1 - Introduction to RAG | Start |
| ✅ Complete | Module 2 - Document Processing | Start |
| ✅ Complete | Module 3 - Embeddings Deep Dive | Start |
| ✅ Complete | Module 4 - Vector Databases | Start |
| ✅ Complete | Module 5 - Retrieval Strategies | Start |
| ✅ Complete | Module 6 - Building RAG Pipelines | Start |
| ✅ Complete | Module 7 - Advanced RAG Techniques | Start |
| ✅ Complete | Module 8 - Evaluation and Optimization | Start |
| ✅ Complete | Module 9 - Production RAG Systems | Start |
| ✅ Complete | Module 10 - Real-World Projects | Start |
🎉 Course Complete!
All modules are ready with complete content and interactive quizzes (30 questions per module).
📖 Additional Resources
| Resource | Description |
|---|---|
| 📚 Glossary | 100+ RAG and AI terms explained |
| 🔗 Resources | Tools, frameworks, papers, and tutorials |
| 🎯 Final Exam | 50 comprehensive questions to test your knowledge |
Get Started!
Explore the modules from the sidebar and begin your journey to mastering RAG systems!
Good luck with your learning journey!
Last updated: December 2024
📊 Course Statistics
| Item | Count |
|---|---|
| Modules | 10 |
| Chapters | 30+ |
| Quiz Questions | 300 |
| Estimated Learning Hours | 40+ |