AI Engineering Interview Questions
Nail interviews on LLMs, Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Databases.
Editorial Integrity
Based on real AI engineering interviews from OpenAI, Anthropic, and leading AI startups.
What is Retrieval-Augmented Generation (RAG) and why do we use it?
RAG is a technique where you search your own database for relevant facts, and then feed those facts to an AI model (like GPT-4) so it can answer a question accurately without hallucinating.
Deep Dive Explanation
Large Language Models (LLMs) are trained on public data up to a certain cutoff date. They don't know your private company data, and they tend to hallucinate when they don't know an answer. RAG solves this by intercepting the user's question, searching a Vector Database (like Pinecone) for relevant internal documents, and then sending both the question AND the relevant documents to the LLM. The LLM acts purely as a synthesizer.
Confusing RAG with 'Fine-tuning'. Fine-tuning changes the model's behavior and tone; RAG gives the model new facts.