Airbnb
Medium
Machine Learning EngineerDesign photo and listing quality models
Machine Learning
2
0
January 5, 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
Roblox
Hard
Data ScientistFit logistic regression and return top features
Machine Learning
1
0
November 23, 2025
OneMain Financial
Hard
Data ScientistHandle missing data and outliers robustly
Machine Learning
2
0
October 13, 2025
Instacart
Hard
Data ScientistContrast Lasso vs Ridge trade‑offs
Machine Learning
2
0
October 13, 2025
Boston Consulting Group
Hard
Data ScientistDetect Data Leakage in Supervised Learning Pipelines
Machine Learning
2
0
August 4, 2025
Amazon
Medium
Data ScientistExplain Overfitting and Underfitting in Machine Learning
Machine Learning
2
0
August 4, 2025
Apple
Medium
Machine Learning EngineerCompare audio preprocessing and training
Machine Learning
2
0
February 15, 2026
Bytedance
Hard
Data ScientistExplain deployment, retrieval, and regularization
Machine Learning
1
0
January 17, 2026
DoorDash
Hard
Data ScientistDesign a Low-Latency Store Recommender
Machine Learning
2
0
January 13, 2026
Salesforce
Medium
Machine Learning EngineerExplain ML and LLM fundamentals
Machine Learning
2
0
January 12, 2026
Microsoft
Easy
Data ScientistExplain KNN and PCA and key tradeoffs
Machine Learning
1
0
November 24, 2025
Meta
Hard
Data ScientistDetect leakage and evaluate a prediction model
Machine Learning
2
0
October 13, 2025
CVS Health
Hard
Data ScientistHandle challenges in MMM/MMX
Machine Learning
1
0
October 13, 2025
Boston Consulting Group
Medium
Data ScientistBuild and evaluate imbalanced binary classifier
Machine Learning
1
0
October 13, 2025
NVIDIA
Medium
Software EngineerCompare ML frameworks and trends
Machine Learning
1
0
September 6, 2025
Microsoft
Medium
Software EngineerCluster city name variants into canonical entities
Machine Learning
2
0
September 6, 2025
Samsara
Medium
Machine Learning EngineerExplain LLM training and evaluation
Machine Learning
1
0
August 14, 2025
Boston Consulting Group
Medium
Data ScientistDifferentiate Overfitting and Underfitting in Machine Learning
Machine Learning
1
0
August 4, 2025
Boston Consulting Group
Easy
Data ScientistScale and Normalize: When to Use Each Method?
Machine Learning
1
0
August 4, 2025
Citibank
Medium
Data ScientistHandle missing values for LGD modeling
Machine Learning
1
0
July 26, 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.