Dhanush B

Posted on Sep 19

Comprehensive RAG Expert Course Curriculum

Course Overview

Duration: 16-20 weeks
Format: Theory + Hands-on Labs
Level: Intermediate to Advanced
Target Audience: Software developers eager to master RAG systems and system design

Phase 1: Foundation (Weeks 1-3)

Module 1: Python for RAG Development

Duration: Week 1-2

Key Topics

Advanced Python concepts (decorators, context managers, async/await)
Data structures for text processing
Memory management & optimization
Error handling & logging

Hands-on Projects

Build a text processing pipeline
Implement custom data structures for document storage
Create async document processors

Tools & Libraries

asyncio, multiprocessing, collections, dataclasses, pydantic, loguru

Learning Resources

Books:

"Fluent Python" by Luciano Ramalho
"Effective Python" by Brett Slatkin

Online:

Python.org Advanced Tutorial
Real Python Pro courses

Module 2: ML & NLP Fundamentals

Duration: Week 2-3

Key Topics

Vector spaces & embeddings
Similarity metrics (cosine, dot product, L2)
Neural network basics
Transformer architecture overview
Attention mechanisms

Hands-on Projects

Implement vector similarity search from scratch
Build a simple transformer encoder
Create embedding visualizations

Tools & Libraries

numpy, scipy, scikit-learn, matplotlib, seaborn, pytorch

Learning Resources

Books:

"Natural Language Processing with Python" by Steven Bird
"Hands-On Machine Learning" by Aurélien Géron

Papers:

"Attention Is All You Need" (Vaswani et al.)

Courses:

CS224N (Stanford NLP)

Phase 2: Core RAG (Weeks 4-6)

Module 3: RAG Architecture & Components

Duration: Week 4-5

Key Topics

RAG pipeline architecture
Document ingestion & preprocessing
Chunking strategies (fixed, semantic, hybrid)
Embedding models comparison
Vector databases overview

Hands-on Projects

Design RAG system architecture
Implement different chunking strategies
Compare embedding models performance
Build a simple vector store

Tools & Libraries

langchain, llama-index, sentence-transformers, tiktoken, spacy, nltk

Learning Resources

Documentation:

LangChain Documentation
LlamaIndex Documentation
Hugging Face Transformers Guide

Papers:

"Retrieval-Augmented Generation" (Lewis et al.)
"Dense Passage Retrieval" (Karpukhin et al.)

Module 4: Vector Databases & Search

Duration: Week 5-6

Key Topics

Vector database architectures
Indexing algorithms (HNSW, IVF, LSH)
Search strategies & filtering
Performance optimization
Metadata handling

Hands-on Projects

Implement HNSW from scratch
Compare vector DB performance
Build hybrid search (vector + keyword)
Create custom indexing strategies

Tools & Libraries

chromadb, pinecone, weaviate, qdrant, faiss, elasticsearch

Learning Resources

Documentation:

Vector DB vendor docs
FAISS documentation

Papers:

"Efficient and Robust Approximate Nearest Neighbor Search" (Malkov & Yashunin)
"Product Quantization" (Jégou et al.)

Phase 3: Prototyping (Weeks 7-9)

Module 5: Rapid RAG Prototyping

Duration: Week 7-8

Key Topics

Framework selection (LangChain vs LlamaIndex)
Prompt engineering for RAG
Context window management
Response synthesis techniques
Basic evaluation metrics

Hands-on Projects

Build 3 different RAG prototypes
A/B test different approaches
Implement custom prompt templates
Create evaluation harness

Tools & Libraries

langchain, llama-index, openai, anthropic, gradio, streamlit

Learning Resources

GitHub Repos:

LangChain templates
LlamaIndex examples
RAG evaluation frameworks

Blogs:

Pinecone Learning Center
LangChain Blog

Module 6: Experimentation & Testing

Duration: Week 8-9

Key Topics

Experiment tracking & versioning
A/B testing frameworks
Automated evaluation pipelines
Retrieval & generation metrics
Human evaluation setups

Hands-on Projects

Build experiment tracking system
Create automated eval pipeline
Design human evaluation interface
Implement statistical testing

Tools & Libraries

mlflow, wandb, dvc, pytest, hypothesis, ragas, trulens

Learning Resources

Documentation:

MLflow Documentation
Weights & Biases Guides

Papers:

"RAGAS: Automated Evaluation of RAG" (Es et al.)
"Evaluating Retrieval-Augmented Generation" (Liu et al.)

Phase 4: Production (Weeks 10-14)

Module 7: Production RAG Architecture

Duration: Week 10-12

Key Topics

Microservices architecture
API design & versioning
Caching strategies (embedding, response)
Queue systems & async processing
Security & authentication

Hands-on Projects

Design production RAG architecture
Implement microservices with FastAPI
Build caching layer with Redis
Create authentication system

Tools & Libraries

fastapi, pydantic, redis, celery, docker, kubernetes, nginx

Learning Resources

Books:

"Designing Data-Intensive Applications" by Martin Kleppmann
"Building Microservices" by Sam Newman

Documentation:

FastAPI documentation
Docker & Kubernetes docs

Module 8: MLOps for RAG

Duration: Week 12-13

Key Topics

Model versioning & registry
CI/CD pipelines for ML
Automated testing strategies
Monitoring & observability
Data drift detection

Hands-on Projects

Build ML pipeline with GitHub Actions
Implement model registry
Create monitoring dashboard
Set up alerting system

Tools & Libraries

mlflow, dvc, github-actions, prometheus, grafana, evidently

Learning Resources

Books:

"Introducing MLOps" by Mark Treveil
"Machine Learning Engineering" by Andriy Burkov

Courses:

MLOps Specialization (Coursera)

Module 9: Performance Optimization

Duration: Week 13-14

Key Topics

Profiling & performance analysis
Memory optimization techniques
Async processing patterns
GPU acceleration
Cost optimization strategies

Hands-on Projects

Profile RAG application bottlenecks
Optimize memory usage
Implement GPU acceleration
Build cost monitoring system

Tools & Libraries

cProfile, py-spy, memory_profiler, torch, cupy, ray

Learning Resources

Documentation:

Python Performance docs
PyTorch optimization guide
Ray documentation

Papers:

GPU acceleration techniques

Phase 5: Scaling (Weeks 15-17)

Module 10: Distributed RAG Systems

Duration: Week 15-16

Key Topics

Distributed vector databases
Load balancing strategies
Sharding & replication
Consistency models
Cross-region deployment

Hands-on Projects

Deploy distributed vector DB
Implement load balancing
Build multi-region system
Create failover mechanisms

Tools & Libraries

kubernetes, helm, istio, consul, etcd, terraform

Learning Resources

Books:

"Designing Distributed Systems" by Brendan Burns
"Database Internals" by Alex Petrov

Documentation:

Kubernetes documentation
Cloud provider guides

Module 11: Enterprise RAG Solutions

Duration: Week 16-17

Key Topics

Multi-tenancy architecture
Enterprise security (SSO, RBAC)
Compliance & governance
Integration patterns
Disaster recovery

Hands-on Projects

Build multi-tenant RAG system
Implement enterprise security
Create compliance monitoring
Design DR procedures

Tools & Libraries

keycloak, vault, istio, fluentd, elasticsearch

Learning Resources

Frameworks:

Enterprise security standards
Compliance documentation

White Papers:

Enterprise AI architecture guides

Phase 6: Advanced (Weeks 17-20)

Module 12: Advanced RAG Techniques

Duration: Week 17-18

Key Topics

Hierarchical retrieval
Multi-modal RAG (text, images, audio)
Adaptive retrieval
Fine-tuning embedding models
Custom LLM integration

Hands-on Projects

Implement hierarchical RAG
Build multi-modal system
Create adaptive retrieval
Fine-tune embedding model

Tools & Libraries

transformers, datasets, accelerate, clip, whisper, unstructured

Learning Resources

Papers:

"Self-RAG" (Asai et al.)
Adaptive Retrieval papers
Multi-modal RAG research

Repositories:

Advanced RAG implementations

Module 13: Research & Innovation

Duration: Week 18-19

Key Topics

Latest RAG research trends
Experimental architectures
Custom loss functions
Novel evaluation methods
Contributing to open source

Hands-on Projects

Implement research paper
Design novel RAG architecture
Create research experiment
Contribute to open source project

Tools & Libraries

Research-specific tools based on chosen papers

Learning Resources

Resources:

ArXiv RAG papers
Google Scholar alerts
ML conferences (NeurIPS, ICML, ACL)
GitHub trending repositories

Module 14: Capstone Project

Duration: Week 19-20

Key Topics

End-to-end RAG system design
Business requirements analysis
Technical implementation
Performance evaluation
Documentation & presentation

Hands-on Projects

Complete production-ready RAG system
Include all course concepts
Deploy to cloud infrastructure
Create comprehensive documentation

Tools & Libraries

All previously learned tools

Learning Resources

Industry Examples:

Real-world RAG case studies
Open source RAG projects
Technical blogs from major companies

Assessment Methods

Assessment Type	Frequency	Weight	Description
Hands-on Labs	Weekly	40%	Practical coding assignments and system implementations
Technical Quizzes	Bi-weekly	20%	Conceptual understanding and best practices
Project Milestones	Monthly	30%	Progressive capstone project deliverables
Final Presentation	End of course	10%	Comprehensive system demonstration and defense

Key Learning Resources Summary

Essential Books

Python: "Fluent Python", "Effective Python"
ML/NLP: "Hands-On Machine Learning", "Natural Language Processing with Python"
System Design: "Designing Data-Intensive Applications", "Building Microservices"
MLOps: "Introducing MLOps", "Machine Learning Engineering"

Critical Papers

"Attention Is All You Need" (Transformer foundation)
"Retrieval-Augmented Generation" (Original RAG paper)
"Dense Passage Retrieval" (DPR)
"Self-RAG" (Advanced techniques)

Industry Resources

Documentation: LangChain, LlamaIndex, Hugging Face, Vector DB vendors
Courses: Stanford CS224N, MLOps specializations
Conferences: NeurIPS, ICML, ACL for latest research
Communities: Reddit r/MachineLearning, Discord servers, GitHub discussions

Course Outcomes

Upon completion of this comprehensive curriculum, learners will have:

Technical Mastery: Deep understanding of RAG architectures, vector databases, and LLM integration
System Design Skills: Ability to design and implement scalable, production-ready RAG systems
MLOps Expertise: Proficiency in deploying, monitoring, and maintaining ML systems in production
Industry Readiness: Hands-on experience with industry-standard tools and best practices
Research Awareness: Understanding of cutting-edge techniques and ability to contribute to the field

This curriculum transforms learners from RAG beginners to industry experts through progressive, hands-on learning with emphasis on system design principles and production-ready implementations.