D Anderson

Can you believe I graduated from college ten years ago today! Where in the world did the time go? 👨🏾‍🎓

🚨 GOOGLE, META, OPENAI etc. BIG TECH are REJECTING JOB CANDIDATES BEFORE EVEN THEY FINISH TALKING. 50 LLM QUESTIONS. IF YOU CAN'T ANSWER THEM, THE INTERVIEW ENDS BEFORE IT STARTS. The people passing these interviews are walking out with $200k+ offers. Someone just LEAKED THE EXACT LLM INTERVIEW QUESTIONS these companies are asking right now. And the gap between people who know these answers and people who do not is already costing careers. Here is every category you need to know: The Basics they always ask first: ↳ How does tokenization work and why does it matter ↳ How does attention actually work inside a transformer ↳ What is a context window and what breaks when it gets too big ↳ What are embeddings and how do they get initialized ↳ How does the model know word order without reading left to right The fine-tuning questions that eliminate 80% of candidates: ↳ What is LoRA and why is it better than full fine-tuning ↳ What is QLoRA and when do you use it instead ↳ How do you fine-tune a model without making it forget everything it already knows ↳ What is model distillation and why do companies use it ↳ How do you handle vocabularies with millions of possible words The generation questions most people guess on: ↳ Beam search vs greedy decoding, which one and when ↳ What temperature actually does to model output ↳ The difference between top-k and top-p sampling ↳ Why autoregressive models work differently from masked models The advanced concepts that separate good from great: ↳ How RAG works and why it beats fine-tuning for factual accuracy ↳ Why Chain-of-Thought prompting makes models dramatically smarter ↳ What Mixture of Experts is and why every frontier model uses it now ↳ Zero-shot vs few-shot learning and when each one wins The math questions that make people sweat: ↳ Why softmax is used inside attention and not something simpler ↳ What cross-entropy loss actually measures ↳ What KL divergence is and where it shows up in AI training ↳ Why vanishing gradients were destroying transformers and how they fixed it If you are applying for any AI role in 2026 and you cannot answer at least 40 of these, you are not ready yet. The full list of 50 questions is worth printing out and going through one by one. Save this post. Your next interviewer has almost certainly pulled from this exact list.

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