Generative AI Interview: Interview Questions, LLMs, RAG, AI Agents, System Design, and Real-World Scenarios

Prepare for Generative AI interviews with a guide that connects core concepts to real-world system design.

Generative AI Interview takes you from transformers and the LLM lifecycle to building, evaluating, securing, and operating AI applications. It is designed to help you explain both how a system works and why you would choose one design over another in an interview.

Across 25 chapters, you'll explore:

• LLM foundations: tokens, attention, training, instruction tuning, model selection, and adaptation with LoRA and QLoRA

• Application development: prompt design, APIs, tool calling, structured outputs, testing, and responsible AI

• RAG systems: embeddings, vector search, hybrid retrieval, reranking, data preparation, evaluation, troubleshooting, and enterprise access controls

• AI agents: planning, memory, orchestration, recovery, secure tool integration, and Model Context Protocol (MCP)

• Production engineering: inference latency, scaling, cost control, observability, versioning, and incident response

The final section brings these topics together through a system design framework and architecture scenarios, including enterprise knowledge assistants, customer support copilots, document analysis platforms, coding assistants, and multimodal applications. You'll also work through reliability, security, and cost scenarios that reflect the decisions engineers face in production.

Interview questions and answers appear throughout the book, followed by role-based questions and mock interviews for Generative AI Engineer, LLM Engineer, RAG Engineer, AI Agent Engineer, AI Platform Engineer, and Generative AI Architect roles. A glossary and architecture and evaluation quick reference support your final review.

Whether you are preparing for an implementation interview or a senior system design discussion, this guide helps you build clear, technically grounded answers to Generative AI questions.