AI Agents & LLMs 2026: RAG Architectures

About this course

This course contains the use of artificial intelligence.

That is not a disclaimer — it is part of the point. The slides and the voice narrating the lectures were produced with AI tools, but every script was written and fact-checked by a human instructor with real hands-on experience. Think of the AI as the production crew; the teaching is human.

Anyone can build a "chat with your documents" demo in an afternoon.

And then you put it in front of real users, on real messy data, and it falls apart. Vague answers. Confident mistakes. "I don't know" when the answer was right there.

Closing that gap is a real engineering discipline. And it has a playbook.

What you will learn:

• RAG Architectures is a clear guide to how retrieval systems actually work.

You'll understand the whole pipeline — chunking, embeddings, vector search, hybrid search, reranking, query transformation, and grounding. For each stage: how it works, where it breaks, and the trade-off.

And you'll learn the one thing that matters most: almost every RAG problem is a retrieval problem. Vendor-neutral, no fluff.

What this course covers, module by module:

Why RAG?; The RAG Pipeline; Chunking; Embeddings; Vector Databases; Retrieval; Why Naive RAG Fails; Hybrid Search; Reranking; Query Transformation; Advanced Indexing; Generation & Grounding; Evaluating RAG; Production RAG.Who this is for:

• This is for builders — anyone who has used a language model and now wants retrieval done right, for production.
• Let's build retrieval that works.
• By the end, you'll understand how a real RAG system is built, debugged, and improved, and know exactly which stage to fix.
• Enroll now.

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