Detailed Introduction to the Retrieval Augmented Generation (RAG) Course

Course Overview

This is a comprehensive course on Retrieval Augmented Generation (RAG) systems offered by DeepLearning.AI, designed to equip learners with the knowledge to develop production-grade RAG applications, from architectural design to deployment and evaluation.

Course Platform: DeepLearning.AI / Coursera
Instructor: Zain Hasan (Senior AI/ML Engineer at Together AI, Lecturer at the University of Toronto)
Course Duration: 5 hours of video + 20+ hours of coding practice
Course Level: Intermediate
Learning Style: Self-paced learning

Instructor Introduction

Zain Hasan is an AI engineer and educator with nearly a decade of experience, having worked at:

• Together AI: As an AI/ML Developer Relations Engineer
• Weaviate: Focusing on vector databases and information retrieval
• University of Toronto: As a lecturer, teaching machine learning systems
• Extensive experience in academia, startups, and the tech industry
• Passionate about open-source software, education, and community building

His teaching style is more akin to learning from an experienced team member than a traditional classroom lecture.

Course Core Content

Three Key Learning Areas

1. Real-world RAG Applications

   • Learn how retrieval and generation work together
   • Design each component to build reliable, flexible RAG systems

2. Search Techniques and Vector Databases

   • Keyword Search
   • Semantic Search
   • Hybrid Search
   • Chunking Techniques
   • Query Parsing
   • Supporting applications in various domains such as healthcare and e-commerce

3. Prompt Design, Evaluation, and Deployment

   • Prompt engineering to fully leverage retrieval context
   • Evaluating RAG system performance
   • Preparing pipelines for production environments

Course Outline (5 Modules)

Module 1: Introduction to RAG

Topics:

• RAG application scenarios
• RAG architecture overview
• Introduction to LLM fundamentals
• Introduction to Python
• Methods for calling LLMs
• Information retrieval basics

Practical Projects:

• Writing retrieval and prompt augmentation functions
• Building your first RAG system
• Passing structured inputs to LLMs

Module 2: Information Retrieval and Search Foundations

Topics:

• Retriever architecture overview
• Metadata Filtering
• Keyword Search (TF-IDF and BM25)
• Semantic Search
• Vector Embeddings in RAG
• Hybrid Search
• Retrieval Evaluation and Metrics

Practical Projects:

• Implementing and comparing Semantic Search, BM25, and Reciprocal Rank Fusion
• Observing the impact of different retrieval methods on LLM responses

Module 3: Information Retrieval with Vector Databases

Topics:

• ANN (Approximate Nearest Neighbor) Algorithms
• Vector Databases
• Introduction to Weaviate API
• Chunking Techniques
• Query Parsing
• Cross-encoders and ColBERT
• Reranking

Practical Projects:

• Extending RAG systems using Weaviate and real-world news datasets
• Performing document chunking, indexing, and retrieval

Module 4: Large Language Models in RAG

Topics:

• Transformer Architecture
• LLM Sampling Strategies
• Exploring LLM Capabilities
• Choosing the Right LLM
• Prompt Engineering
• Addressing Hallucinations
• Evaluating LLM Performance
• Agentic RAG
• RAG vs. Fine-tuning

Practical Projects:

• Developing a domain-specific chatbot for a virtual clothing store
• Answering FAQs and providing product recommendations based on custom datasets
• Using open-source LLMs hosted by Together AI

Module 5: Production, Evaluation, and Deployment

Topics:

• Production Challenges
• Implementing RAG Evaluation Strategies
• Logging, Monitoring, and Observability
• RAG System Tracing
• Custom Evaluation
• Quantization Techniques
• Cost vs. Response Quality Trade-offs
• Latency vs. Response Quality Trade-offs
• Security
• Multimodal RAG

Practical Projects:

• Handling real-world challenges like dynamic pricing
• Logging user interactions for monitoring and debugging
• Improving chatbot reliability

Tech Stack & Tools

Core Tools

• Vector Database: Weaviate
• LLM Platform: Together AI (Open-source LLMs)
• Monitoring Tool: Phoenix (Arize)
• Development Language: Python

Key Technologies Involved

- Vector Embeddings
- Semantic Search
- BM25 Keyword Search
- Hybrid Search (TF-IDF + Semantic)
- Reciprocal Rank Fusion
- Cross-encoders
- ColBERT
- Chunking Strategies
- Query Parsing
- Reranking
- Prompt Engineering
- Quantization

Practical Application Areas

The course uses real-world datasets from the following domains:

• 📰 Media: News datasets
• 🏥 Healthcare: Medical documents
• 🛍️ E-commerce: Product data, pricing information
• 📊 Finance: Financial documents

Learning Outcomes

Upon completing the course, you will be able to:

✅ Design and implement all components of a complete RAG system
✅ Select the right architecture for your use case
✅ Utilize vector databases like Weaviate
✅ Experiment with prompting and retrieval strategies
✅ Monitor performance using tools like Phoenix
✅ Understand key trade-offs:

• When to use hybrid retrieval
• How to manage context window limitations
• How to balance latency and cost

✅ Evaluate and iteratively improve RAG pipelines
✅ Adapt to new methods and the evolving ecosystem
✅ Transition from proof-of-concept to practical deployment

Prerequisites

• Required: Intermediate Python skills
• Recommended: Foundational knowledge of Generative AI
• Recommended: High school level mathematics

Course Features

🎯 Practice-Oriented

• 5 progressive coding labs
• From simple prototypes to production-grade components
• Real-world datasets

📚 Systematic Learning

• Covers component-level and system-level techniques
• Understand fundamental principles and practical trade-offs
• Adapt to the rapidly evolving RAG ecosystem

🏆 Earn Certification

Upon completion, you will receive a certificate from DeepLearning.AI, certifying your skills in building and evaluating RAG systems using real-world tools and techniques.

Importance of RAG

Why is RAG Needed?

While large language models are powerful, they often make mistakes without the correct information. RAG addresses this by:

1. Grounding responses: Basing model responses on relevant, often private or up-to-date data
2. Accessing external knowledge: Retrieving relevant information not included in the LLM's training
3. Improving accuracy: Using domain-specific, private, or current knowledge bases

RAG Application Scenarios

• 🔧 Internal Tools: Enterprise knowledge base queries
• 💬 Customer Service Assistants: Support based on product documentation
• 🎯 Specialized Applications: Expert systems in fields like healthcare, legal, and finance
• 📱 Personalized Assistants: Customized services based on user data

Learning Tips

As recommended by DeepLearning.AI:

1. Create a dedicated learning space: Establish a quiet, organized, and distraction-free workspace
2. Establish a consistent study schedule: Set fixed study times and build a habit
3. Take regular breaks: Use the Pomodoro Technique (25 minutes study + 5 minutes break)
4. Engage with the community: Join forums, discussion groups, and community events
5. Learn actively: Take notes, summarize, teach others, or apply in real-world projects

Course Access

• Official Website: https://www.deeplearning.ai/courses/retrieval-augmented-generation-rag/
• Coursera Platform: https://www.coursera.org/learn/retrieval-augmented-generation-rag
• Learning Platform: https://learn.deeplearning.ai/courses/retrieval-augmented-generation

Recommended Related Courses

If you are interested in RAG, you might also consider:

• Building and Evaluating Advanced RAG Applications
• Knowledge Graphs for RAG
• LangChain: Chat with Your Data
• Building Multimodal Search and RAG
• Building Agentic RAG with LlamaIndex

Summary

This is a comprehensive and in-depth RAG course, ideal for those who wish to:

• Engineers transitioning from POC to production environments
• Developers building reliable, scalable LLM applications
• AI practitioners understanding RAG system design trade-offs
• Learners mastering the latest RAG techniques and tools

The course not only teaches technical implementation but also focuses on developing systemic thinking and engineering decision-making skills, helping learners remain competitive in the evolving RAG ecosystem.