Intermediate~14 hours14 lessons

Production RAG Engineer

Build, evaluate, debug, and deploy real-world retrieval-augmented generation systems. Go beyond toy examples to handle chunking tradeoffs, hybrid retrieval, reranking, citation grounding, and RAG observability.

Start learning Practice quiz

What you'll learn

Document ingestion pipelines

Chunking strategies and tradeoffs

Embedding model selection

Vector database setup and indexing

Hybrid retrieval (sparse + dense)

Reranking models and latency tradeoffs

Query rewriting and HyDE

Citations and grounded answers

Permission-aware retrieval

RAG evaluation datasets and metrics

Hallucination reduction techniques

RAG observability and tracing

Cost and latency optimization

Production RAG capstone project

Learning outcomes

1Design a production RAG pipeline end to end

2Choose chunking strategies for different document types

3Implement hybrid retrieval with BM25 and dense vectors

4Evaluate RAG quality with faithfulness, recall, and precision metrics

5Debug hallucinations and retrieval failures systematically

Related lessons

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Fine-Tuning vs RAG

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AI System Design Interview Basics

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Prerequisites

AI Engineer Foundation

Practice quizzes

Rag quiz Vector Databases quiz Evaluation quiz

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