Zillow
Medium
Machine Learning EngineerExplain why LLMs produce hallucinations
Machine Learning
4
0
September 24, 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 EngineerExplain overfitting, imbalance, undersampling, and attention heads
Machine Learning
2
0
August 8, 2025
Boston Consulting Group
Medium
Data ScientistDifferentiate Overfitting and Underfitting in Machine Learning
Machine Learning
2
0
August 4, 2025
Shopify
Medium
Machine Learning EngineerModel Product Ranking
Machine Learning
2
0
April 1, 2026
Cvs
Easy
Data ScientistReduce LLM hallucination and handle class imbalance
Machine Learning
4
0
October 17, 2025
Capital One
Hard
Data ScientistPresent and defend your data challenge end-to-end
Machine Learning
4
0
October 13, 2025
Other
Hard
Data ScientistContrast L1 and L2 regularization effects
Machine Learning
3
0
October 13, 2025
Creditkarma
Medium
Machine Learning EngineerDebug Sparse Multi-Task Ranking Models
Machine Learning
2
0
June 10, 2026
Two Sigma
Easy
Data ScientistHow to forecast bike dock demand
Machine Learning
4
0
February 13, 2026
TikTok
Medium
Software EngineerExplain RL policy types and modern policy gradients
Machine Learning
4
0
January 11, 2026
Amazon
Hard
Data ScientistDesign end-to-end regression for energy demand
Machine Learning
3
0
October 13, 2025
TikTok
Hard
Data ScientistExplain and tune XGBoost; prevent overfitting
Machine Learning
2
0
October 13, 2025
Yelp
Hard
Data ScientistDesign a News Feed with APIs
Machine Learning
2
0
October 13, 2025
Amazon
Hard
Data ScientistCompare Random Forests vs Gradient Boosting rigorously
Machine Learning
4
0
October 13, 2025
Amazon
Hard
Data ScientistApply Double ML with text-address features
Machine Learning
4
0
October 13, 2025
Snapchat
Hard
Machine Learning EngineerExplain core ML concepts and design choices
Machine Learning
3
0
September 6, 2025
Capital One
Medium
Data ScientistEvaluate Python Class Design in Data Pipeline
Machine Learning
2
0
August 4, 2025
Citibank
Medium
Data ScientistHandle missing values for LGD modeling
Machine Learning
2
0
July 26, 2025
Amazon
Hard
Software EngineerExplain attention and Transformers
Machine Learning
3
0
July 15, 2025
Amazon
Medium
Software EngineerExplain core ML fundamentals
Machine Learning
4
0
July 15, 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.