Retrieval-Augmented Generation (RAG) presentation

1. Retrieval-Augmented Generation
(RAG)
• Enhancing LLMs with External Knowledge

2. Motivation
• LLMs have fixed knowledge
• Hallucination issues
• No private data access

3. What is RAG?
• RAG = Retrieval + Generation
• Grounded responses

4. Architecture
• Retriever
• Vector DB
• LLM Generator

5. Working
• Query → Embedding → Retrieval → Prompt →
Answer

6. Document Processing
• Chunking
• Embeddings
• Vector Storage

7. Advantages
• Low hallucination
• No retraining
• Domain-specific

8. Applications
• Legal
• Healthcare
• Education
• Enterprise

9. Challenges
• Latency
• Context limit
• Retrieval quality

10. Summary
• RAG makes LLMs reliable and scalable