AI Engineering Interview Questions
Nail interviews on LLMs, Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Databases. Prepare with these 1 real-world questions covering beginner to advanced scenarios.
What is Retrieval-Augmented Generation (RAG) and why do we use it?
Simple Answer
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.
Detailed Answer
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.
Interview Scenario Walkthrough
My Thinking Process:
"Identify the problem: LLMs lack private data. Solution: Fetch the data dynamically and inject it into the prompt."
Possible Causes:
- LLM cutoff dates
- Data privacy
- Hallucinations
How I Would Answer:
"RAG stands for Retrieval-Augmented Generation. I'd use it to let an LLM securely answer questions based on our proprietary databases without having to retrain the model."
Interview Tip
Emphasize that RAG solves the 'hallucination' problem and the 'private data' problem simultaneously. It's the standard for enterprise AI apps.
Common Mistake
Confusing RAG with 'Fine-tuning'. Fine-tuning changes the model's behavior and tone; RAG gives the model new facts.
Real World Example
If an employee asks an internal HR bot, 'What is the maternal leave policy?', a standard LLM might guess based on general US law. A RAG system will search the company's internal HR PDF, pull out the exact paragraph on maternal leave, give it to the LLM, and the LLM will reply: 'According to our 2026 handbook, you get 16 weeks of paid leave.'
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Question 1 of 1What is Retrieval-Augmented Generation (RAG) and why do we use it?
Keep Building Your AI Engineering Skills
Interview prep works best when combined with hands-on practice. Use these resources to deepen your understanding and build portfolio projects.