Netflix
Medium
Data ScientistAddress Fraud Detection with Imbalance and Concept Drift Solutions
Machine Learning
13
0
July 12, 2025
LLM & Generative AI Interview Questions
LLM and generative AI questions are rapidly growing in interview frequency as companies adopt AI-first strategies.
Expect questions on transformer architecture, attention mechanisms, fine-tuning strategies, RAG pipelines, and evaluation of generative models.
Interviewers at AI companies like Anthropic, OpenAI, and Google evaluate both theoretical depth and practical deployment experience.
500Questions
107Companies
Easy 1 • Medium 9 • Hard 196Difficulty mix
Common LLM interview patterns
- Transformer architecture and self-attention mechanism
- Fine-tuning vs prompting vs RAG trade-offs
- Retrieval-Augmented Generation (RAG) pipeline design
- Prompt engineering and chain-of-thought reasoning
- Evaluation metrics for generative models (BLEU, ROUGE, human eval)
- Tokenization strategies and vocabulary design
- Alignment, RLHF, and safety considerations
LLM interview questions
Filter and sort
Google
Medium
Data ScientistAdjust YouTube Ad Scores Using Mixed-Effects Linear Regression
Machine Learning
8
0
July 12, 2025
LinkedIn
Medium
Data ScientistExplain variance reduction in random forests
Machine Learning
6
0
February 19, 2026
Amazon
Medium
Machine Learning EngineerExplain Transformers and MoE in LLMs
Machine Learning
5
0
December 8, 2025
Microsoft
Hard
Data ScientistImplement robust k-means from scratch
Machine Learning
3
0
October 13, 2025
Google
Medium
Data ScientistAddress Overfitting in Supervised Learning Models
Machine Learning
13
0
July 12, 2025
Microsoft
Medium
Machine Learning EngineerHow do you choose a model?
Machine Learning
4
0
March 10, 2026
Rippling
Medium
Software EngineerFind minimum of unknown convex function
Machine Learning
3
0
December 8, 2025
PayPal
Hard
Machine Learning EngineerAssess LLMs for fraud detection
Machine Learning
5
0
September 6, 2025
TikTok
Medium
Data ScientistPredict Customer Churn with Machine Learning Workflow
Machine Learning
17
0
July 12, 2025
NVIDIA
Medium
Machine Learning EngineerExplain bias-variance, calibration, and model drift
Machine Learning
2
0
February 11, 2026
TikTok
Medium
Machine Learning EngineerExplain your VLM project end-to-end
Machine Learning
3
0
December 15, 2025
Tesla
Medium
Software EngineerHow to Identify Best Battery Group
Machine Learning
5
0
October 26, 2025
Uber
Hard
Data ScientistSelect the better $5 promo-targeting model
Machine Learning
5
0
October 13, 2025
Netflix
Medium
Data ScientistDesign Real-Time Fraud Detection with XGBoost Model
Machine Learning
5
0
August 4, 2025
Capital One
Medium
Data ScientistEvaluate Models for Credit-Risk Scoring at Capital One
Machine Learning
14
0
August 4, 2025
Amazon
Medium
Data ScientistOptimize Feature Selection and Handling in Machine Learning Models
Machine Learning
17
0
July 12, 2025
Two Sigma
Hard
Data ScientistPredict bike demand and avoid overfitting
Machine Learning
4
0
March 13, 2026
Apple
Medium
Machine Learning EngineerExplain dataset size, generalization, and U-Net skips
Machine Learning
5
0
March 1, 2026
Jane Street
Medium
Data ScientistDesign a Real-vs-Fake DNA Classifier
Machine Learning
2
0
January 27, 2026
Common mistakes in LLM interviews
- Not understanding the difference between fine-tuning and in-context learning
- Ignoring hallucination risks in production deployments
- Overcomplicating solutions when prompt engineering suffices
- Not discussing latency, cost, and token budget trade-offs
- Treating LLMs as deterministic systems
How LLM questions are evaluated
Show practical understanding of when to use fine-tuning vs RAG vs prompting.
Discuss evaluation strategies for open-ended generation tasks.
Demonstrate awareness of safety, alignment, and deployment considerations.
Related ML concepts
LLM & Generative AI Interview FAQs
What is RAG and how does it differ from fine-tuning?
RAG (Retrieval-Augmented Generation) retrieves relevant documents at inference time and provides them as context to the LLM. Fine-tuning modifies the model weights on your data. RAG is better for frequently changing knowledge; fine-tuning is better for teaching the model new skills or styles.
What transformer concepts should I know for interviews?
Understand self-attention, multi-head attention, positional encoding, and the encoder-decoder architecture. Know why attention scales better than RNNs for long sequences. Be able to explain how the key-query-value mechanism works intuitively.