What is RAG?

What you will be able to do

• Explain when retrieval-augmented generation fits better than prompting or fine-tuning
• Trace documents through ingestion, chunking, embedding, indexing, retrieval, and generation
• Design grounded prompts with useful citations and safe refusal behavior
• Diagnose retrieval, freshness, conflict, injection, and unsupported-synthesis failures
• Evaluate retrieval and answer quality separately and tune the correct stage
• Review a RAG architecture with a practical production checklist

Lessons in this topic

1. 01RAG is an open-book system13m
2. 02From documents to a searchable index15m
3. 03Retrieval, query flow, and top-k14m
4. 04Augmentation, prompting, and citations14m
5. 05Worked case: employee policy assistant16m
6. 06RAG failure modes and defenses15m
7. 07Evaluate retrieval and answers separately16m
8. 08Mastery check: review a RAG architecture14m

Related lessons

• What are embeddings?
• Fine-tuning vs RAG
• Chunking for RAG quality
• What is a large language model?

Sources

• Lewis et al. — Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• OWASP — LLM Prompt Injection Prevention Cheat Sheet