Cracked my Senior AI Engineer interview using this guide! The practical scenarios on RAG pipeline optimization and fine-tuning techniques like LoRA gave me a massive edge during technical rounds.
Ananya IyerData Scientist, Hyderabad YOUR NEXT CHAPTER STARTS HERE
Less overwhelm.
More “I’ve got this.”
Generative AI Interview Questions.
Detailed answers, diagrams, and Python examples for your next role.
117-page PDF ebook 2026 edition
YOUR INTERVIEW PREP, IN ONE BOOK
117 pages · 19 sections · PDF
ONE BOOK. A COMPLETE STUDY PATH.
Generative AI Interview Questions
Start with the fundamentals. Connect the concepts. Practice explaining your decisions.
An 117-page interview preparation ebook spanning 19 topic sections, from LLM fundamentals and prompt engineering to RAG, agents, production systems, and practical Python. Includes a final revision checklist. 2026 edition.
117 pages19 topic sections2026 edition
Inside the ebook
- GenAI & LLM Fundamentals
- Prompt Engineering
- Embeddings & Vector Databases
- RAG (Retrieval-Augmented Generation)
- RAG Troubleshooting
- LLM Evaluation
- Fine-Tuning & Model Adaptation
- Quantization & LLM Inference
- AI Agents
- MCP, Tool Ecosystems & Agent Integration
- Multi-Agent Systems
- Multimodal GenAI
- GenAI Security
- Production GenAI / LLMOps
- Cost Optimization
- GenAI System Design
- Coding / Practical GenAI
- Python + GenAI Engineering
- Scenario-Based Questions
Includes a final revision checklist
FROM THE READING DESK
What readers are saying.
Selected reader feedback shared with InterviewLeaf.
Most prep books stick to basic ML, but this one strictly targets modern GenAI. The system design architecture breakdowns for LLM applications were clear, precise, and directly applicable.
Rohan DeshmukhSoftware Engineer, PuneTransitioning into GenAI felt overwhelming until I picked up this book. The Q&A layout breaks down complex topics like vector embeddings, context window limits, and prompt engineering into easy-to-digest answers.
Priya NairAI Solutions Architect, KochiThe section on LLM evaluation metrics and AI guardrails is top-tier. It covers real-world enterprise constraints that engineering managers actually ask about during senior-level interviews.
Vikram PatelTech Lead, GurugramStudy questions and explanations for interview preparation.