RAG (Retrieval-Augmented Generation) Tutorial

1. RAG Introduction

What Retrieval-Augmented Generation is, the problem it solves, and the query-retrieve-generate flow.

2. Embeddings & Vector Databases

What embeddings are, cosine similarity, and how vector databases differ from normal databases.

3. Building a RAG Pipeline

Chunking documents, embedding and storing them, retrieving top-k chunks, and generating a grounded answer.

4. Advanced Retrieval Techniques

Hybrid search combining keyword and vector search, reranking, and query expansion for better retrieval.

5. Common RAG Failure Modes

Lost-in-the-middle, chunk boundary problems, and stale indexes -- concrete examples and how to fix each.

6. Evaluating RAG Systems

Retrieval metrics like precision/recall@k, and evaluating generated answers for relevance and faithfulness.

7. Production RAG Architecture

A full production architecture: the ingestion pipeline, chunking strategy, caching layers, and monitoring.

8. RAG Interview Questions

Commonly asked RAG interview questions with clear, practical answers.