Amazon
Medium
Machine Learning EngineerHandle cold start, dropout, and training stability
Machine Learning
4
0
January 6, 2026
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
Microsoft
Easy
Machine Learning EngineerExplain bias-variance and evaluate a classifier
Machine Learning
4
0
December 27, 2025
Coinbase
Medium
Machine Learning EngineerBuild a baseline classification model from messy data
Machine Learning
4
0
December 4, 2025
Expedia
Hard
Data ScientistValidate and monitor ranking model end-to-end
Machine Learning
4
0
October 13, 2025
Boston Consulting Group
Medium
Data ScientistBuild a leak-free sklearn pipeline
Machine Learning
4
0
October 13, 2025
Amazon
Easy
Machine Learning EngineerList hyperparameter tuning methods
Machine Learning
6
0
December 8, 2025
Tesla
Hard
Machine Learning EngineerCompare RNNs, LSTMs, Transformers, and MPC
Machine Learning
2
0
September 6, 2025
Snapchat
Medium
Machine Learning EngineerExplain core ML fundamentals and tradeoffs
Machine Learning
4
0
January 10, 2026
SoFi
Hard
Data ScientistDesign a fintech product ranking system
Machine Learning
5
0
October 13, 2025
Apple
Medium
Data ScientistDesign Siri-vs-GPT query routing
Machine Learning
3
0
January 4, 2026
TikTok
Hard
Machine Learning EngineerDefine QKV for recommender cross-attention
Machine Learning
4
0
December 8, 2025
Apple
Medium
Data ScientistBuild leak-safe sklearn model with calibration
Machine Learning
3
0
October 13, 2025
Amazon
Medium
Data ScientistCompare Regularization Techniques and Their Use Cases
Machine Learning
12
0
July 12, 2025
Snapchat
Medium
Machine Learning EngineerModel an ads ranking system
Machine Learning
4
0
February 12, 2026
Google
Hard
Data ScientistPredict and act on contract renewal risk
Machine Learning
4
0
October 13, 2025
TikTok
Hard
Data ScientistExplain SHAP vs VIF under collinearity
Machine Learning
2
0
October 13, 2025
Netflix
Medium
Machine Learning EngineerExplain tokenization and Transformer variants
Machine Learning
3
0
August 13, 2025
Apple
Medium
Machine Learning EngineerCompare DCN v1 vs v2 and A/B test
Machine Learning
3
0
March 1, 2026
Capital One
Medium
Data ScientistHow would you design delay and watchlist models?
Machine Learning
3
0
January 30, 2026
Google
Hard
Data ScientistBuild and evaluate illegal-video classifier
Machine Learning
4
0
October 13, 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.