Databricks
Hard
Data ScientistExplain ROC-AUC vs PR-AUC tradeoffs
Machine Learning
3
0
December 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.
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
TikTok
Hard
Machine Learning EngineerDefine QKV for recommender cross-attention
Machine Learning
5
0
December 8, 2025
CVS Health
Hard
Data ScientistDesign classification under missingness and imbalance
Machine Learning
3
0
October 13, 2025
Boston Consulting Group
Medium
Data ScientistBuild and evaluate imbalanced binary classifier
Machine Learning
2
0
October 13, 2025
Intuit
Hard
Data ScientistDecide when to model courier ETA
Machine Learning
5
0
October 13, 2025
XPeng
Hard
Machine Learning EngineerExplain PPO and Transformer basics
Machine Learning
5
0
September 6, 2025
Boston Consulting Group
Hard
Data ScientistDetect Data Leakage in Supervised Learning Pipelines
Machine Learning
5
0
August 4, 2025
Dandy
Medium
Machine Learning EngineerExplain ML fundamentals (activations, CV, vision, sorting)
Machine Learning
5
0
January 17, 2026
Amazon
Easy
Machine Learning EngineerExplain vanishing gradients and activations
Machine Learning
4
0
December 8, 2025
Thumbtack
Medium
Data ScientistDetail NLP preprocessing and n‑gram choices
Machine Learning
5
0
October 13, 2025
Citibank
Medium
Data ScientistDiagnose and fix linear regression assumption breaks
Machine Learning
5
0
October 13, 2025
Startups.Com
Medium
Machine Learning EngineerExplain attention variants and their tradeoffs
Machine Learning
2
0
March 10, 2026
Other
Hard
Data ScientistExtract companies from noisy text
Machine Learning
3
0
October 13, 2025
Freddie Mac
Hard
Data ScientistDesign a hierarchical MF delinquency forecasting system
Machine Learning
3
0
October 13, 2025
Microsoft
Hard
Data ScientistCompare CNN, RNN, and LSTM rigorously
Machine Learning
3
0
October 13, 2025
Amazon
Medium
Data ScientistExplain K-Fold Cross-Validation and Its Trade-Offs
Machine Learning
5
0
August 4, 2025
Capital One
Medium
Data ScientistIdentify Risks and Improve Imputation Class Implementations
Machine Learning
4
0
August 4, 2025
PayPal
Hard
Data ScientistBuild a real-time ATO model
Machine Learning
4
0
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain Collaborative Filtering Approaches
Machine Learning
6
0
September 6, 2025
Two Sigma
Medium
Data ScientistHow detect duplicate card records?
Machine Learning
2
0
February 26, 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.