Intuit
Hard
Data ScientistDecide when to model courier ETA
Machine Learning
4
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
Citadel
Hard
Data ScientistDesign regression and classification ML pipelines
Machine Learning
5
0
September 6, 2025
Amazon
Hard
Data ScientistChoose Between Fine-Tuning and RAG for Client Chatbot
Machine Learning
4
0
August 4, 2025
Amazon
Hard
Data ScientistDerive and compare core ML and RL methods
Machine Learning
4
0
October 13, 2025
Spotify
Medium
Machine Learning EngineerCompare Unsupervised Clustering Methods
Machine Learning
2
0
March 4, 2026
Uber
Medium
Data ScientistModel Driver Acceptance Probability
Machine Learning
3
0
February 27, 2026
Google
Hard
Data ScientistModel Shot Success by Location
Machine Learning
2
0
December 3, 2025
Amazon
Easy
Data ScientistExplain core ML concepts and metrics
Machine Learning
3
0
October 11, 2025
Hudson
Hard
Software EngineerBuild a Heart Disease Baseline
Machine Learning
2
0
March 13, 2026
Capital One
Medium
Machine Learning EngineerExplain core ML concepts and lifecycle
Machine Learning
4
0
December 15, 2025
Amazon
Medium
Machine Learning EngineerExplain ML evaluation, sequence models, and optimizers
Machine Learning
3
0
December 15, 2025
CVS Health
Hard
Data ScientistDesign classification under missingness and imbalance
Machine Learning
2
0
October 13, 2025
Microsoft
Medium
Machine Learning EngineerExplain metrics, regularization, and ablation studies
Machine Learning
3
0
February 9, 2026
Waymo
Medium
Software EngineerDesign an Online Experiment
Machine Learning
4
0
February 6, 2026
Uber
Medium
Machine Learning EngineerImplement Multi-Head Self-Attention
Machine Learning
4
0
January 10, 2026
Citadel
Hard
Data ScientistDesign city home-price prediction system
Machine Learning
3
0
October 13, 2025
Boston Consulting Group
Medium
Data ScientistDesign and sample for credit default prediction
Machine Learning
2
0
October 13, 2025
Microsoft
Hard
Data ScientistCompare CNN, RNN, and LSTM rigorously
Machine Learning
2
0
October 13, 2025
LinkedIn
Medium
Data ScientistDesign a short-video recommendation system
Machine Learning
2
0
October 12, 2025
Applied Intuition
Medium
Machine Learning EngineerImplement correct attention masking
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.