Building and Evaluating Advanced RAG

About this course

Retrieval-Augmented Generation (RAG) is transforming how AI systems access and use knowledge—from customer support bots to research assistants. This course, from DeepLearning.AI, dives into advanced RAG techniques that go beyond basic retrieval, teaching you sentence window retrieval and auto-merging strategies to dramatically improve how your systems find and rank the most relevant information. Whether you're building production systems or exploring AI's frontier, mastering these techniques is essential for creating smarter, more reliable AI applications.

What you'll learn

• How sentence window retrieval works and when to use it to improve context precision in RAG systems
• Auto-merging retrieval techniques that intelligently combine retrieved chunks for richer context windows
• Practical methods to evaluate and measure retrieval quality—moving beyond intuition to data-driven assessment
• How to diagnose and fix common retrieval failures in production RAG pipelines
• Real-world trade-offs between retrieval speed, relevance, and computational cost
• Hands-on implementation patterns you can apply immediately to your own projects

Who this is for

You're a developer, researcher, or AI practitioner ready to level up from basic RAG to production-grade systems. This course assumes you understand the fundamentals of how RAG works—if you're brand new to RAG, start with a beginner course first.

• AI engineers and ML practitioners — master advanced retrieval techniques to ship faster, more reliable AI products into production
• Data scientists and researchers — learn evaluation frameworks and optimization strategies to push retrieval accuracy and system performance further

Prerequisites

Familiarity with basic RAG concepts (what retrieval and generation are), Python programming, and foundational understanding of embeddings or vector search. You don't need a PhD in NLP—just hands-on experience building or deploying a simple RAG system.

Why this matters for Indian learners

India's AI and software services sector is booming, with companies like TCS, Infosys, and emerging startups racing to integrate RAG into customer-facing products. As generative AI moves from hype to production, RAG engineers are in high demand—and advanced retrieval skills command premium roles and salaries. Whether you're aiming for a role at a Bangalore AI startup, consulting on enterprise AI, or building your own product, this specialized knowledge sets you apart in a competitive job market.

Frequently asked questions

Is this course really free?

Yes—completely free. No hidden fees, no paywalls for certificates. You can audit the entire course at no cost.

How long will it take to complete?

The core content is about 1 hour. Most learners complete it in one sitting or spread it across a couple of evenings. Budget extra time if you want to experiment with the code examples on your own machine.

Will I get a certificate?

This course doesn't offer a formal certificate of completion. But you'll gain real, verifiable skills—focus on implementing what you learn in a real project to showcase your expertise to employers.