AI Interview MasteryIntermediate → SeniorNEW

RAG Systems

Embeddings, vector stores, and retrieval strategies for RAG. From basic similarity search to advanced hybrid retrieval, re-ranking, and production-grade RAG architectures.

4.9rating2,780 students1h 50m total24 lessons

Start Course

What you'll learn

Explain RAG from first principles and why it outperforms fine-tuning for factual queries

Build a chunking and embedding pipeline from scratch

Implement hybrid search combining BM25 and vector similarity

Apply re-ranking to improve retrieval precision

Evaluate RAG quality using recall@K and MRR

Design a production RAG system for a domain-specific knowledge base

Final Project

Build and evaluate a RAG system for a pharmaceutical knowledge base: chunk, embed, index, retrieve, and measure recall@5

Curriculum

24 lessons · 1h 50m

What is RAG and Why Does It Exist?

10 min

RAG vs Fine-Tuning: When to Choose Each

12 min

The Full RAG Pipeline: Index → Retrieve → Generate

15 min

What Can Go Wrong in a RAG System

10 min

Course Info

Lessons24 lessons

Total time1h 50m

LevelIntermediate → Senior

Students2,780

Rating4.9 / 5.0

Start Course — Free