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AI Interview MasteryIntermediate → SeniorNEW
Fine-Tuning LLMs
Fine-tuning fundamentals, techniques, and trade-offs for interviews. From full fine-tuning to LoRA, QLoRA, and RLHF — every technique an AI engineer is expected to understand and explain.
4.8rating1,760 students1h 20m total16 lessons
What you'll learn
Explain the difference between pre-training, fine-tuning, and RLHF
Describe LoRA and QLoRA and why they're preferred over full fine-tuning
Prepare a dataset for fine-tuning: format, size, quality requirements
Choose between fine-tuning and RAG for a given use case
Evaluate a fine-tuned model using the right benchmarks
Describe the risks of fine-tuning: catastrophic forgetting, overfitting
Final Project
Answer 10 fine-tuning interview questions back-to-back, including one scenario: should this use case use fine-tuning or RAG?
Curriculum
16 lessons · 1h 20mCourse Info
Lessons16 lessons
Total time1h 20m
LevelIntermediate → Senior
Students1,760
Rating4.8 / 5.0