Amazon
Medium
Machine Learning EngineerExplain surprisal and its units
Machine Learning
2
0
November 20, 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
Meta
Hard
Data ScientistDeploy multi-armed bandits safely
Machine Learning
2
0
October 13, 2025
Other
Hard
Data ScientistTune metrics for imbalanced classification
Machine Learning
1
0
October 13, 2025
Dropbox
Medium
Data ScientistDesign enterprise file recommendations under ACLs
Machine Learning
1
0
October 13, 2025
Meta
Easy
Data ScientistChoose clustering for social network users
Machine Learning
1
0
October 11, 2025
Amazon
Hard
Software EngineerExplain attention and Transformers
Machine Learning
1
0
July 15, 2025
Intuit
Easy
Product AnalystHandle missing and unavailable predictive features
Machine Learning
1
0
October 21, 2025
Meta
Medium
Data ScientistChoose metrics for fake-user classifier
Machine Learning
1
0
October 13, 2025
Thumbtack
Hard
Data ScientistBuild a defensible ML pipeline end-to-end
Machine Learning
1
0
October 13, 2025
Other
Hard
Data ScientistPredict job changes month by month
Machine Learning
1
0
October 13, 2025
NVIDIA
Hard
Software EngineerExplain optimization and tensor vs pipeline parallelism
Machine Learning
1
0
September 6, 2025
Amazon
Hard
Data ScientistEvaluate RAG System Accuracy and Cost Control Strategies
Machine Learning
1
0
August 4, 2025
Capital One
Medium
Data ScientistEvaluate Python Class Design in Data Pipeline
Machine Learning
1
0
August 4, 2025
Meta
Medium
Data ScientistIdentify Algorithms for Detecting Malicious Duplicated Content
Machine Learning
2
0
August 4, 2025
C3 AI
Medium
Data ScientistExplain key ML/stats concepts
Machine Learning
1
0
July 25, 2025
Amazon
Hard
Data ScientistDesign a robust traffic forecasting pipeline
Machine Learning
2
0
October 13, 2025
Other
Medium
Data ScientistEvaluate and select K in K-means
Machine Learning
1
0
October 13, 2025
Other
Hard
Data ScientistDetect clickbait without labels, then supervise
Machine Learning
1
0
October 13, 2025
Other
Hard
Data ScientistExplain OS usage gap via trees
Machine Learning
2
0
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain ML basics: imbalance, metrics, bias-variance
Machine Learning
1
0
September 6, 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.