Comprehensive RAG Expert Course Curriculum

This content originally appeared on DEV Community and was authored by Dhanush B

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

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:

1. Technical Mastery: Deep understanding of RAG architectures, vector databases, and LLM integration
2. System Design Skills: Ability to design and implement scalable, production-ready RAG systems
3. MLOps Expertise: Proficiency in deploying, monitoring, and maintaining ML systems in production
4. Industry Readiness: Hands-on experience with industry-standard tools and best practices
5. 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.

This content originally appeared on DEV Community and was authored by Dhanush B