Amazon
Hard
Data ScientistDesign a robust traffic forecasting pipeline
Machine Learning
3
0
October 13, 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.
525Questions
115Companies
Easy 52 • Medium 261 • Hard 198Difficulty 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
Tencent
Hard
Data ScientistDesign email to avoid Promotions without online tests
Machine Learning
1
0
October 13, 2025
First American
Medium
Data ScientistDescribe Building and Deploying a Machine Learning Model
Machine Learning
3
0
August 4, 2025
TikTok
Medium
Data ScientistPredict User Churn with Effective Modeling Techniques
Machine Learning
2
0
August 4, 2025
Snapchat
Medium
Machine Learning EngineerExplain Overfitting and Transformer Attention
Machine Learning
2
0
April 29, 2026
TikTok
Medium
Software EngineerExplain DPO and construct its training data
Machine Learning
2
0
December 8, 2025
Other
Medium
Data ScientistCompare trees, RF, and gradient boosting
Machine Learning
3
0
October 13, 2025
Capital One
Hard
Data ScientistBuild and validate a binary classifier
Machine Learning
3
0
October 13, 2025
TikTok
Hard
Data ScientistDesign an ad-selection system across objectives
Machine Learning
3
0
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain imbalance, metrics, bias-variance, Transformers vs. CNNs
Machine Learning
3
0
September 6, 2025
J.P. Morgan
Medium
Data ScientistExplain Overfitting and Transformer Basics
Machine Learning
3
0
April 7, 2026
Morgan Stanley
Medium
Data ScientistExplain Chunking for Financial RAG
Machine Learning
2
0
March 1, 2026
UiPath
Easy
Machine Learning EngineerExplain Core ML Fundamentals
Machine Learning
1
0
January 10, 2026
Uber
Medium
Data ScientistDesign a ride-hailing ETA system
Machine Learning
1
0
January 3, 2026
Intuit
Easy
Product AnalystHandle missing and unavailable predictive features
Machine Learning
2
0
October 21, 2025
Meta
Medium
Data ScientistPropose an ads recommendation model for shop ads
Machine Learning
3
0
October 14, 2025
Meta
Hard
Data ScientistDesign hashtag recommender with cold start
Machine Learning
2
0
October 13, 2025
Capital One
Medium
Data ScientistExplain MSE vs MAE, AUC, and imbalance handling
Machine Learning
3
0
October 13, 2025
TikTok
Medium
Data ScientistCompare bagging vs boosting on imbalanced data
Machine Learning
3
0
October 13, 2025
Meta
Hard
Data ScientistDesign a System to Recommend Local Restaurant Profiles
Machine Learning
2
0
August 4, 2025
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.