← AI Agents & RAG learning pathAI Agents & RAG · Module 4 of 10
Retrieval-Augmented Generation
Ground LLM answers in your own documents.
📖 Key ideas
- RAG retrieves relevant chunks and adds them to the prompt.
- Steps: load documents → chunk → embed → store → retrieve → generate.
- Reduces hallucinations and adds up-to-date knowledge.
- Ask the model to cite sources.
- No retraining needed when documents change.
💡 Real-life example: RAG is an open-book exam: the model looks up the right pages before answering.
RAGRetrieval-Augmented Generation
ChunkPiece of a document
RetrieverFinds relevant chunks
GroundingBasing answers on given sources