Voleon
Hard
Machine Learning EngineerExplain ML and statistical modeling
Machine Learning
7
0
February 15, 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.
522Questions
115Companies
Easy 52 • Medium 258 • Hard 198Difficulty 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
Google
Medium
Machine Learning EngineerExplain LLM lifecycle and trade-offs
Machine Learning
9
0
January 19, 2026
Snapchat
Medium
Machine Learning EngineerCompare convolutions and transformers
Machine Learning
9
0
August 13, 2025
Upstart
Hard
Data ScientistDesign a Real-Time Personalized Ad Selection System
Machine Learning
6
0
August 4, 2025
Meta
Medium
Data ScientistEmploy Collaborative Filtering for Personalized Recommendation Lists
Machine Learning
39
0
July 12, 2025
Microsoft
Easy
Data ScientistCompute and plot a precision–recall curve
Machine Learning
6
0
January 17, 2026
Apple
Hard
Machine Learning EngineerExplain annotation agreement and LLM vs human judges
Machine Learning
6
0
August 13, 2025
Citadel
Hard
Machine Learning EngineerMake a hard MoE router differentiable
Machine Learning
12
0
July 15, 2025
Upstart
Easy
Data ScientistExplain L1 vs L2 and ridge vs lasso
Machine Learning
8
0
December 9, 2025
Apple
Medium
Data ScientistDesign Siri-vs-GPT query routing
Machine Learning
8
0
January 4, 2026
Capital One
Medium
Data ScientistBuild and evaluate airline delay prediction model
Machine Learning
10
0
October 13, 2025
Google
Hard
Machine Learning EngineerList regularization methods and trade-offs
Machine Learning
10
0
September 6, 2025
Microsoft
Easy
Machine Learning EngineerExplain bias-variance and evaluate a classifier
Machine Learning
9
0
December 27, 2025
Uber
Hard
Data ScientistBuild and assess CTR prediction
Machine Learning
8
0
October 13, 2025
Uber
Medium
Machine Learning EngineerExplain XGBoost depth, regularization, and dropout
Machine Learning
6
0
September 6, 2025
Netflix
Medium
Data ScientistDesign Real-Time Fraud Detection with XGBoost Model
Machine Learning
11
0
August 4, 2025
PayPal
Hard
Machine Learning EngineerAssess LLMs for fraud detection
Machine Learning
10
0
September 6, 2025
Amazon
Easy
Data ScientistDesign and evaluate a RAG system
Machine Learning
10
0
January 12, 2026
DoorDash
Medium
Machine Learning EngineerHow would you target promotions to grow consumers?
Machine Learning
9
0
October 17, 2025
Google
Medium
Data ScientistBuild Classifier: Evaluate with AUROC for Imbalanced Data
Machine Learning
29
0
July 12, 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.