Citadel
Medium
Data ScientistExplain RF optimization and variable-importance pitfalls
Machine Learning
2
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.
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
Walmart Labs
Medium
Data ScientistPredict Next-Period Conversion Rate Using Historical Campaign Data
Machine Learning
2
0
August 4, 2025
DRW
Hard
Machine Learning EngineerBuild an imbalanced classification pipeline with sklearn
Machine Learning
3
0
July 31, 2025
Uber
Easy
Machine Learning EngineerExplain and test completion-rate gaps
Machine Learning
2
0
March 9, 2026
Amazon
Easy
Machine Learning EngineerCompare decision trees and random forests
Machine Learning
2
0
December 8, 2025
Roblox
Hard
Data ScientistNormalize features and rank logistic coefficients
Machine Learning
3
0
November 24, 2025
Bytedance
Medium
Data ScientistBuild and iteratively improve sentiment classifier
Machine Learning
3
0
November 12, 2025
Instacart
Hard
Data ScientistImprove low R² without p‑hacking
Machine Learning
2
0
October 13, 2025
NVIDIA
Hard
Data ScientistDiagnose overfitting, DenseNet, preprocessing, CV
Machine Learning
1
0
October 13, 2025
Zillow
Medium
Machine Learning EngineerExplain why LLMs produce hallucinations
Machine Learning
2
0
September 24, 2025
TikTok
Hard
Machine Learning EngineerExplain overfitting, imbalance, undersampling, and attention heads
Machine Learning
1
0
August 8, 2025
Microsoft
Medium
Data ScientistExplain Deep Learning to a 5-Year-Old Child
Machine Learning
2
0
August 4, 2025
DRW
Hard
Machine Learning EngineerExplain Transformers, activations, and training optimization
Machine Learning
1
0
July 29, 2025
Microsoft
Easy
Data ScientistExplain SHAP and build an ML project
Machine Learning
2
0
February 9, 2026
Etsy
Hard
Data ScientistDesign ML ranking for query suggestions
Machine Learning
2
0
October 13, 2025
Meta
Hard
Data ScientistChoose and compute recommender evaluation metrics
Machine Learning
2
0
October 13, 2025
Other
Hard
Data ScientistContrast L1 and L2 regularization effects
Machine Learning
2
0
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain Collaborative Filtering Approaches
Machine Learning
3
0
September 6, 2025
Startups.Com
Medium
Machine Learning EngineerExplain attention variants and their tradeoffs
Machine Learning
1
0
March 10, 2026
Amazon
Easy
Data ScientistDesign and evaluate a RAG system
Machine Learning
2
0
January 12, 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.