What it does This repo collects interview questions and answers for AI engineering roles: LLM fundamentals, prompt engineering, RAG, agents, fine-tuning, vector DBs, LLMOps, and the usual behavioral suspects. It targets titles like “AI Engineer,” “LLM Engineer,” and “Agentic AI Engineer”—whatever your recruiter typed into LinkedIn this week.

The interesting bit The answers are largely outbound links to the author’s own content ecosystem—Outcome School blog posts, YouTube videos, and social threads. It’s a study guide that doubles as a content funnel, which is either clever marketing or a minor conflict of interest depending on your cynicism level.

Key highlights

• Covers the full stack: transformers, attention math, KV cache, Flash Attention, MoE, RoPE, quantization, RLHF variants (PPO, DPO, GRPO)
• Includes practical troubleshooting scenarios: “Your LLM keeps ignoring instructions,” “Your chatbot loses context after 10 turns”
• Sections on newer terrain: MCP, recursive language models, continual learning, large reasoning models
• Claims it will “keep updating” with new questions

Caveats

• Many answers are just links to external content rather than self-contained explanations
• Some questions have no answers at all (WordPiece, Top-p vs Top-k, several scenario questions)
• Heavy self-promotion of Outcome School courses and paid programs

Verdict Useful if you want a structured checklist of what to study and don’t mind clicking through to videos and blog posts. Skip it if you need deep, self-contained explanations or if referral-link fatigue sets in quickly.