The AI Interview - Master AI/ML Interviews

What is RAG and why is it useful?

Examples

Example 1:

Input: Can you explain the concept of Retrieval-Augmented Generation (RAG)?

Output: [A clear, concise explanation of Retrieval-Augmented Generation (RAG)]

Explanation: A direct interview question testing foundational knowledge of the topic.

Example 2:

Input: What are the practical implications or challenges associated with Retrieval-Augmented Generation (RAG)?

Output: [Discussion of trade-offs, advantages, or real-world issues related to Retrieval-Augmented Generation (RAG)]

Explanation: A follow-up interview question assessing depth of understanding and practical experience.

Starter Code

/* Hint: Think about the core concepts of Retrieval-Augmented Generation (RAG), how it works under the hood, and its impact on LLM performance or capabilities. */