RAG Interview Questions for Freshers

RAG combines a retrieval system with an LLM: retrieve relevant documents from a knowledge base, then pass them as context to the LLM to generate answers. It's better than fine-tuning for: (1) Up-to-date knowledge — add new docs without retraining, (2) Source attribution — you can cite which document answered the question, (3) Cost — no expensive GPU training, (4) Interpretability — you can debug what was retrieved. Fine-tuning is better for style, tone, and domain-specific reasoning patterns.