Sealth
Easy
Machine Learning EngineerImplement Beam Search With Length Normalization
Machine Learning
1
0
April 19, 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
Imc
Medium
Data ScientistExplain linear regression and Transformer fundamentals
Machine Learning
1
0
January 14, 2026
TikTok
Easy
Data ScientistHow do you choose a classification threshold?
Machine Learning
1
0
November 8, 2025
Tradedesk
Easy
Data ScientistDesign a CVR model for RTB bidding
Machine Learning
1
0
October 18, 2025
Meta
Hard
Data ScientistDesign a hashtag recommender for News Feed
Machine Learning
1
0
October 13, 2025
Yelp
Hard
Data ScientistDesign a News Feed with APIs
Machine Learning
0
0
October 13, 2025
Yelp
Hard
Data ScientistDesign a Cold-Start-Aware Recommender
Machine Learning
0
0
October 13, 2025
Meta
Medium
Data ScientistTune fraud threshold under review capacity and costs
Machine Learning
1
0
October 13, 2025
TikTok
Hard
Data ScientistContrast LSTM and Transformer for long sequences
Machine Learning
1
0
October 13, 2025
TikTok
Hard
Data ScientistEstimate heterogeneous treatment effects with causal ML
Machine Learning
1
0
October 13, 2025
Spokeo
Hard
Data ScientistDesign Comprehensive Recommendation System for Spokeo Features
Machine Learning
1
0
August 4, 2025
NVIDIA
Medium
Software EngineerCompare deep learning framework trends
Machine Learning
1
0
July 31, 2025
Amazon
Medium
Software EngineerExplain deep learning and transformer concepts
Machine Learning
0
0
May 28, 2025
Bytedance
Easy
Data ScientistExplain train-test generalization gap
Machine Learning
1
0
March 9, 2026
Oracle
Medium
Data ScientistExplain Medical AI Data and Evaluation
Machine Learning
0
0
January 20, 2026
Imc
Medium
Data ScientistRun EDA and train models while preventing overfitting
Machine Learning
1
0
January 14, 2026
Meta
Medium
Data ScientistDesign an ad recommendation and ranking system
Machine Learning
1
0
October 20, 2025
Amazon
Hard
Data ScientistExplain random forests, bagging, and evaluation
Machine Learning
1
0
October 13, 2025
TikTok
Hard
Data ScientistDesign an ad-selection system across objectives
Machine Learning
1
0
October 13, 2025
Meta
Medium
Data ScientistEvaluate Classifier with Precision, Recall, and Fairness Metrics
Machine Learning
1
0
August 4, 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.