LLM Fine-Tuning for AI Engineer Interviews — Complete Beginner Masterclass (15 Must-Know Questions)

Preparing for an AI/ML engineer interview? Fine-tuning large language models is one of the topics that separates people who have read about LLMs from people who have actually trained them. In this complete beginner masterclass, we work through the 15 fundamental fine-tuning questions interviewers really use — with the background, the intuition, and the "out-loud" answer a strong candidate would give. No fluff. Every answer is built from first principles with clear visuals, and includes the common traps that quietly sink real training runs. WHAT YOU'LL LEARN The difference between pretraining, SFT, instruction tuning, and alignment — and why order matters When to fine-tune vs. when to just prompt (or use retrieval) Catastrophic forgetting, the epoch myth, and how to read a loss curve Loss masking, chat templates, and how a single training example is really structured Full fine-tuning vs. parameter-efficient fine-tuning (LoRA) — and what "efficient" actually saves How to choose learning rate, epochs, and batch size — and how decoding settings affect evaluation CHAPTERS 00:00 Introduction — What fine-tuning really is 02:41 Q1 — Pretraining vs Continued Pretraining vs SFT vs Alignment (why order matters) 05:35 Q2 — Fine-tune vs Prompt: what each uniquely solves 08:10 Q3 — Catastrophic forgetting (why narrow data is worse) 10:55 Q4 — The epoch myth: why more epochs can hurt 13:06 Q5 — Loss masking: train on the answer, not the question 15:33 Q6 — Chat templates: the #1 silent killer of fine-tunes 18:14 Q7 — Anatomy of one training example (special tokens & EOS) 20:33 Q8 — Quality over quantity: why 1k clean beats 100k noisy (LIMA) 23:10 Q9 — Full fine-tuning vs PEFT / LoRA: what "efficient" saves 25:37 Q10 — Loss near zero: red flag or trophy? 27:49 Q11 — Good loss, bad output: a ranked debugging checklist 30:01 Q12 — Choosing learning rate, epochs & batch size 32:20 Q13 — Warmup & learning-rate schedules 34:30 Q14 — Overfitting: the first interventions 36:43 Q15 — Decoding (temperature / top-k / top-p) & evaluation 39:23 Key Takeaways & what's next WHO THIS IS FOR Aspiring and current AI/ML engineers, data scientists, and researchers preparing for interviews — or anyone who wants a solid, intuition-first mental model of how LLM fine-tuning actually works. This is Part 1 of a series. Intermediate (LoRA internals, QLoRA, DPO vs RLHF) and Architect tiers are coming next — subscribe so you don't miss them. If this helped, leave a like and a comment with the question you found trickiest. #LLM #MachineLearning #AIEngineer #FineTuning #LoRA #DeepLearning #MLInterview #GenAI #NLP #ArtificialIntelligence