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LLM Fine-Tuning Interview Questions Interview Guide

11 interview questions with sample answers

14-18 hours
Prep Time
$150K-$240K
Salary
11
Questions

About This Role

Master LLM fine-tuning: data preparation, training strategies, evaluation, cost optimization, and deploying fine-tuned models in production.

Behavioral Questions (3)

Q1

Tell me about a time you fine-tuned a model. Why was fine-tuning better than prompting?

Sample Answer:

Fine-tuned GPT-3.5 for customer support classification. Prompting achieved 72% accuracy. Fine-tuning with 2K examples reached 91%. Cost justified: $500 fine-tuning cost vs weeks of prompt engineering.

Q2

How have you prepared training data for fine-tuning? What challenges did you face?

Sample Answer:

Collected 5K support tickets, annotated labels, balanced classes, created train/val split (4K/1K). Challenges: label inconsistency (implemented review process), class imbalance (weighted sampling), data privacy (anonymization).

Q3

Describe your process for evaluating a fine-tuned model before production.

Sample Answer:

Evaluated on held-out test set: accuracy, precision, recall by class. Compared against baseline prompting. Tested edge cases manually. Did A/B test with 10% users before full rollout.

Technical & Situational Questions (4)

Q4

How do you handle training data imbalance in fine-tuning?

Sample Answer:

Use stratified sampling during split. Implement class weights during training. Over-sample minority class. Create synthetic examples for rare cases. Monitor validation loss per class.

Q5

Explain the differences between LoRA, QLoRA, and full fine-tuning.

Sample Answer:

Full: all weights updated, best accuracy, high memory. LoRA: adapter layers, 10-50x less memory, 95% of accuracy. QLoRA: quantized, 4-bit, runs on consumer hardware. Choose LoRA for production, QLoRA for prototyping.

Q6

How would you fine-tune an LLM for domain-specific language?

Sample Answer:

Continue pre-training on domain corpus, then supervised fine-tuning on task examples. Domain-specific vocabulary matters. Monitor perplexity on domain text. Use domain experts to validate quality.

Q7

What metrics do you track during fine-tuning training?

Sample Answer:

Training loss, validation loss, accuracy, task-specific metrics (F1 for classification). Monitor for overfitting (diverging train/val loss). Use early stopping. Validate on out-of-distribution test set.

FAQ

How much training data do I need for fine-tuning?
Minimum 100 examples, but 500-2K is typical. More data improves accuracy but with diminishing returns. At 10K+ examples, compare cost vs training a small model from scratch.
Should I fine-tune or use prompting?
Fine-tune if: domain-specific patterns, need deterministic style, accuracy critical. Use prompting if: one-time task, rapid iteration needed, cost-sensitive, avoiding infrastructure.
How do I prevent overfitting in fine-tuning?
Use validation set, implement early stopping, regularization (weight decay), limit epochs, use data augmentation, evaluate on diverse test set, incorporate human feedback.
What's the cost-benefit of fine-tuning?
OpenAI fine-tuning: $0.03-0.30 per 1K tokens input (depending on model size). Cost amortizes over high-volume use. Break-even often <10K inferences. Calculate ROI before committing.

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Last updated on 2026-03-07