Generative AILesson 6
Lesson 69 minPro
RAG (Retrieval-Augmented Generation)
Give an LLM your own up-to-date, private knowledge.
What you will learn
- ✓Why RAG beats fine-tuning for facts
- ✓The retrieve-then-generate flow
- ✓What a vector database does
Explanation
LLMs only know what they were trained on, and they can't cite your private documents. RAG fixes this without retraining.
The flow: store your documents as embeddings in a vector database; when a question comes in, retrieve the most relevant chunks; paste them into the prompt; let the LLM answer using that context.
RAG is how 'chat with your PDFs/company docs' products work — accurate, current, and grounded in sources.
Real-world use
A support bot that answers from your latest help-centre articles almost always uses RAG, not a fine-tuned model.
Common mistakes
- • Reaching for expensive fine-tuning when RAG would add fresh knowledge far more cheaply.
Practice
Sketch a RAG pipeline for a chatbot that answers from your company's policy PDFs.
Knowledge check
0/2 answered1. RAG works by...
2. A vector database stores...
Answer all questions to check.