AI Interview Guide for Full Stack Developers - 2026 EditionLLMs, RAG, Agents, MCP & Production AIThe Full Stack Developer Guide Series - D. Walse
AI questions are now in every full stack interview. This book is how you answer them.
In 2026, you can't get through a technical loop without being asked how an LLM actually works, how you'd design a RAG pipeline, how you'd stop prompt injection, or how you use AI coding assistants day to day. This is a complete, interview-focused preparation guide for developers at every level - from freshers facing their first screening round to senior engineers and architects walking into system design panels.
What's inside
- 160 in-depth interview questions and answers across 16 chapters, organized by difficulty (Beginner, Intermediate, Advanced), with the most commonly asked questions clearly flagged
- 400 rapid-fire Q&As - 25 per chapter - for fast revision the night before
- 24 professional diagrams covering the transformer, the RAG pipeline, the agent loop, MCP architecture, HNSW search, prompt injection paths, and production AI system design
- Coding challenges and system design walkthroughs, including a tool-calling loop, a semantic cache, a token-budget conversation manager, an AI coding assistant backend, and an enterprise document-Q&A platform
- A 30-day preparation plan, a final readiness checklist, and a 73-term glossary
Every answer follows the same battle-tested structure: a complete spoken answer, key points, a worked example, follow-up questions the interviewer will actually ask next, common mistakes that sink candidates, and a panel tip on how to deliver it.
The 16 chapters
AI Fundamentals - Large Language Models - Prompt Engineering - AI Coding Assistants - AI for Frontend - AI for Backend - AI Agents - Model Context Protocol (MCP) - RAG - Vector Databases & Embeddings - AI APIs & Integration - AI Security - AI System Design - AI Performance - AI Deployment & LLMOps - Interview Scenarios & Coding Challenges
Current for 2026
This edition covers what interviewers are asking now: reasoning models and test-time compute, agentic coding tools, the Model Context Protocol, GraphRAG and query transformation, the OWASP LLM Top 10, excessive agency, denial-of-wallet attacks, slopsquatting, evaluation-driven development, LoRA fine-tuning, prefill vs. decode, continuous batching, and treating cost as a first-class design constraint.
Written for how interviews are actually won
This isn't a machine learning textbook, and it isn't a list of trivia. It teaches you to reason out loud like a senior engineer: lead with requirements, name the trade-off, pair every risk with a mitigation, and know when not to use a technique. It's honest about limits - where AI-generated code fails, when RAG beats fine-tuning, why guardrails belong in code and not the prompt, and why calibrated honesty beats bluffing in the room.
Practical, current, and built entirely around what gets asked.
