Reuters
Easy
Software EngineerDiscuss overfitting, contrastive learning, transformers
Machine Learning
1
0
November 14, 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
Medium
Data ScientistWhich clustering algorithm would you use and why
Machine Learning
1
0
November 2, 2025
TikTok
Hard
Data ScientistDetect and suppress bad sellers robustly
Machine Learning
1
0
October 13, 2025
Twitch
Hard
Data ScientistDesign real-time live-stream recommendations
Machine Learning
1
0
October 13, 2025
UiPath
Hard
Machine Learning EngineerExplain transformer architecture and FFN rationale
Machine Learning
1
0
September 6, 2025
Amazon
Hard
Machine Learning EngineerExplain key ML concepts and techniques
Machine Learning
1
0
July 31, 2025
Intuit
Easy
Software EngineerWhen should products use AI?
Machine Learning
1
0
April 2, 2026
Google
Medium
Machine Learning EngineerExplain LLM lifecycle and trade-offs
Machine Learning
1
0
January 19, 2026
Meta
Medium
Data ScientistPropose an ads recommendation model for shop ads
Machine Learning
1
0
October 14, 2025
Amazon
Medium
Data ScientistWrite and explain gradient descent pseudocode
Machine Learning
1
0
October 13, 2025
Tencent
Hard
Data ScientistDesign email to avoid Promotions without online tests
Machine Learning
0
0
October 13, 2025
Capital One
Hard
Data ScientistEvaluate and monitor a credit risk model
Machine Learning
1
0
October 13, 2025
Amazon
Hard
Data ScientistDesign fraud detection across channels with unknowns
Machine Learning
1
0
October 13, 2025
Meta
Hard
Data ScientistDesign a restaurant recommender under cold start
Machine Learning
1
0
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain Logistic Regression Fundamentals
Machine Learning
0
0
September 6, 2025
Snapchat
Medium
Machine Learning EngineerCompare convolutions and transformers
Machine Learning
1
0
August 13, 2025
NewsBreak
Hard
Machine Learning EngineerDesign approach for class imbalance
Machine Learning
0
0
August 9, 2025
Spokeo
Medium
Data ScientistUnderstand Bias-Variance Trade-off and Regularization Techniques
Machine Learning
1
0
August 4, 2025
Meta
Medium
Data ScientistDetect and Reduce Spammy Friend Requests Effectively
Machine Learning
1
0
August 4, 2025
Sealth
Easy
Machine Learning EngineerImplement Beam Search With Length Normalization
Machine Learning
1
0
April 19, 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.