2 Weeks
·Cohort-based Course
Master the skills to build and deploy production-ready RAG systems, combining AI and search for real-world, scalable applications.
2 Weeks
·Cohort-based Course
Master the skills to build and deploy production-ready RAG systems, combining AI and search for real-world, scalable applications.
Previously worked with
Course overview
In this course, you'll gain the practical skills to design, build, and prototype Retrieval-Augmented Generation (RAG) systems that can scale for real-world applications. Starting with core concepts, you'll work through hands-on projects, mastering techniques to combine search and AI models for powerful, efficient systems. By the end, you'll be equipped to confidently deploy RAG systems in production environments, solving real business problems with cutting-edge AI.
What to expect in the course:
Week 1
Module 1: Introduction to Retrieval-Augmented Generation
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✅ Welcome - Introduction to this course
~ Course Overview - What to Expect
~ Tools and techstack - eg. Python, LLM Providers, NBDev, and Qdrant
~ Prerequisites - Python Proficiency, Machine Learning Basics (JavaScript Helpful but Optional)
✅ RAG Systems Overview - Setting the Stage
~ Defining RAG - The Core Idea Behind the System
~ Why RAG? - The Power of Combining Retrieval and Generation
~ Conceptual Flow of RAG Systems
✅ Historical Evolution of RAG Systems
~ Development of Generative Models - A Brief History
~ Rise of Retrieval-Augmented Systems - How We Got Here
## QA-Retriever-Reader vs.
## QA-Retriever-Generator
✅ RAG System Architecture - Key Concepts and Components
✅ When to Apply RAG - Key Use Cases Explored
✅ Challenges and Limitations of RAG Systems
Module 2: Architecture and Components of RAG Systems
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✅ Understanding the RAG Pipeline - An Overview
✅ Data Flow in RAG Systems - How Information Moves
✅ The Retriever - The Heart of RAG Systems
~ Dense vs. Sparse Retrieval - Key Differences Explained (e.g., BM25, DPR, etc.)
~ Exploring Retrieval Methods for RAG Pipelines
~ Knowledge Stores - Understanding Vector Databases
✅ The Generator Component - Creating Responses
~ Generation Models Overview - Powering the Generator
~ Input Representation - Preparing Data for Generation
✅ Bringing It All Together - The Full RAG Workflow
✅ Hands-On Practice - Setting Up a Basic RAG Pipeline
Module 3: Preparing and Ingesting Data for RAG Systems
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✅ Parsing Raw Documents: The First Step to Understanding
✅ Extracting Key Metadata from Documents
✅ Document Chunking: Structuring Data for Retrieval
✅ Embedding Document Chunks for Efficient Search (Transformers, OpenAI, Jina, Nomic, etc.)
✅ Indexing Document Embeddings in a Vector Database
Week 2
Module 4: Building a Complete RAG Pipeline
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✅ Implementing RAG Using Popular Frameworks
~ RAG with LlamaIndex
~ RAG with LangChain
~ RAG with Haystack
✅ Building RAG from Scratch: A Comparison with Frameworks
✅ Advanced RAG Techniques
~ Enhancing Retrieval with Query Expansion & Rewriting
~ Optimizing Results through Query Re-Routing
~ Improving Accuracy with Re-Ranking Strategies
~ Boosting Efficiency through Caching
~ Refining Retrieval Over Time by harnessing Feedback Loops
~ Exploring Other Advanced Retrieval Techniques (Raptor, Agentic RAG, Corrective RAG, HyDE etc.)
✅ Integrating RAG: Front-End and Back-End Development
✅ Hands-On Project: Build a Research Paper Chatbot from Scratch
🌶️ Bonus: Scaling RAG Systems
Module 5: Evaluation and Fine-tuning
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✅ Evaluating RAG Systems: Metrics and Methods (Precision, recall, AP@k, quality, response time, MRR, NDCG, hit rate, human evaluation)
✅ Optimizing LLMs for Enhanced Accuracy
✅ When and How to Fine-Tune Your RAG Pipeline
Module 6: Observability and Cost
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✅ Common Issues in LLM Applications (Low recall, noisy results)
✅ Understanding Observability in LLM Applications (Logging, user feedback etc.)
✅ LLM Monitoring vs. Observability: Key Differences (Latency, error rate, user engagement)
✅ Practical Guide: Setting Up Observability for LLM Applications
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AI Engineers & Developers: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models.
02
Data Scientists: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications.
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Tech Leads & Product Managers: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems
Introduction to RAG systems
Get a solid foundation in Retrieval-Augmented Generation (RAG), designed for novice learners. You'll grasp the basics of combining AI with search and build confidence in developing production-ready systems.
Build your first RAG system from scratch
Walk through step-by-step to create your first RAG system, even with no prior experience. This course covers all the fundamental concepts you need to start deploying real-world AI solutions.
Design production-ready RAG systems
Learn to design scalable Retrieval-Augmented Generation systems using search and AI, ready for real-world applications. Gain practical skills to manage system components and optimize performance.
Optimize RAG for real-world use cases
Learn how to fine-tune and optimize RAG systems for specific industries and use cases, from customer support to content generation. By the end, you'll be able to tailor AI solutions to meet diverse business needs.
6 interactive live sessions
Lifetime access to course materials
In-depth lessons
Direct access to instructor
Projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Mastering RAG Systems: A Hands-on Guide to Production-Ready AI
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@AwakenwithoutCoffee
Egil Sandfeld
Mehdi is a Chief AI Engineer, accomplished AI Researcher, and winner of Anthropic’s 2024 AI Developer Contest. With over a decade of experience in AI, LLM, NLP, and ML, he has led complex AI implementations across finance, technology, and gaming sectors. Previously a Computer Science professor at Georgia Southern University, Mehdi has earned over 2000 citations for his research and conference best paper awards. He has also collaborated with esteemed institutions like the National Science Foundation on advanced AI solutions. Mehdi is the published author of A Practical Approach to Retrieval-Augmented Generation Systems (Oct 2023) and co-host of the TwoSetAI YouTube channel, where he shares cutting-edge RAG techniques.
Angelina is a seasoned full-stack data scientist and AI startup founder. She is a two-time fast.ai fellow under Jeremy Howard and holds five patents in machine learning and natural language processing. As a former VP of data and ML, Angelina has built and led high-performing data science teams across industries like finance, fintech, and gaming. She is also a published author and Substack writer, with works on Retrieval-Augmented Generation and Machine Learning Interviews. Winner of Anthropic’s 2024 AI Developer Contest, Angelina continues to share her expertise as co-host of the TwoSetAI YouTube channel.
Join an upcoming cohort
Cohort 1
$1,500
Dates
Payment Deadline
4 - 6 hours per week
Live Sessions on Saturdays & Sundays
6:00pm - 7:30 pm EST
Concept and code walk throughs of building a RAG system end-to-end.
Starting Oct 26, 2024
For 2 weeks
Weekly Projects and Office Hours
3 hours per week
Office hours: 1 hour per week
Weekly project: 2 - 4 hours per week
Offline Contents
1 hour per week
Good reads to take home for your busy schedule.
Ebook: A Practical Approach to Retrieval Augmented Generation Systems
This free eBook provides a foundational understanding of Retrieval-Augmented Generation (RAG) systems. It covers real-world applications, such as using RAG for interacting with PDF documents, and explores various frameworks like LLamaIndex, LangChain, and Haystack. It also addresses practical challenges in deploying RAG systems. While this book lays the groundwork, our course will offer the latest updates and advanced techniques. Download it to build a solid base before diving deeper with us.
Get this free resource
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Join an upcoming cohort
Cohort 1
$1,500
Dates
Payment Deadline