Disney
Medium
Machine Learning EngineerBuild Naive Bayes spam classifier with F1
Machine Learning
2
0
November 1, 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
Zoox
Medium
Data ScientistDesign navigation-safety simulation parameters and experiments
Machine Learning
3
0
October 13, 2025
Amazon
Hard
Data ScientistBuild a package-allocation model for couriers
Machine Learning
2
0
October 13, 2025
Freddie Mac
Hard
Data ScientistDesign a hierarchical MF delinquency forecasting system
Machine Learning
2
0
October 13, 2025
Atlassian
Medium
Data ScientistTrain and evaluate logistic model with regularization
Machine Learning
2
0
October 13, 2025
Google
Hard
Data ScientistExplain a favorite model end-to-end
Machine Learning
2
0
October 13, 2025
Point72
Hard
Data ScientistExplain and tune decision trees robustly
Machine Learning
1
0
October 13, 2025
SoFi
Hard
Data ScientistDesign a fintech homepage ranker
Machine Learning
1
0
October 13, 2025
Amazon
Medium
Machine Learning EngineerExplain LLM architecture, tuning, evaluation
Machine Learning
2
0
September 6, 2025
TikTok
Hard
Machine Learning EngineerImplement attention and nucleus sampling; compare to top-k
Machine Learning
3
0
August 11, 2025
TikTok
Medium
Data ScientistChoose Between Random Forests and Gradient Boosting Models
Machine Learning
2
0
August 4, 2025
Snapchat
Hard
Machine Learning EngineerExplain LLM tuning and transformer basics
Machine Learning
3
0
January 30, 2026
PayPal
Hard
Data ScientistExplain fraud types and evaluate a fraud model
Machine Learning
1
0
January 2, 2026
Amazon
Easy
Machine Learning EngineerAnalyze attention complexity and improvements
Machine Learning
1
0
December 8, 2025
Databricks
Easy
Data ScientistCompare ROC-AUC vs PR-AUC
Machine Learning
3
0
December 3, 2025
Zillow
Medium
Machine Learning EngineerExplain challenges in training multimodal LLMs
Machine Learning
3
0
November 8, 2025
Cvs
Easy
Data ScientistReduce LLM hallucination and handle class imbalance
Machine Learning
3
0
October 17, 2025
Capital One
Hard
Data ScientistPresent and defend your data challenge end-to-end
Machine Learning
3
0
October 13, 2025
Snapchat
Hard
Data ScientistExplain Random Forest randomness and implications
Machine Learning
3
0
October 13, 2025
Other
Hard
Data ScientistDesign a hybrid marketplace fraud system
Machine Learning
2
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.