PayPal
Medium
Data ScientistIdentify Unsupervised Techniques for Detecting Fraudulent Transactions
Machine Learning
134
0
August 4, 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.
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
Data ScientistBuild Model to Predict Customer Contract Renewal
Machine Learning
112
0
July 12, 2025
Uber
Easy
Machine Learning EngineerExplain and test completion-rate gaps
Machine Learning
21
0
March 9, 2026
Google
Medium
Machine Learning EngineerExplain ranking cold-start strategies
Machine Learning
32
0
March 30, 2026
Nextdoor
Medium
Machine Learning EngineerBuild a model using only pandas/numpy
Machine Learning
29
0
March 1, 2026
Uber
Hard
Data ScientistOptimize Surge Notifications for Rideshare Drivers
Machine Learning
93
0
August 4, 2025
Google
Medium
Data ScientistEngineer Features to Enhance Smartphone Battery Life Prediction
Machine Learning
101
0
July 12, 2025
Coinbase
Hard
Data ScientistHow to Analyze and Model Behavioral Data Effectively?
Machine Learning
102
0
July 12, 2025
Waymo
Easy
Data ScientistImplement K-means and handle train-inference mismatch
Machine Learning
15
0
December 6, 2025
Apple
Hard
Software EngineerImplement multi-head self-attention correctly
Machine Learning
17
0
September 6, 2025
Voleon Group
Hard
Data ScientistBuild a regularized regression pipeline
Machine Learning
11
0
October 13, 2025
Google
Medium
Data ScientistCompare Logistic Regression and Random Forest in Limited Data Scenarios
Machine Learning
95
0
July 12, 2025
Scale AI
Hard
Machine Learning EngineerExplain Transformers, attention, decoding, RL, and evaluation
Machine Learning
21
0
September 6, 2025
Meta
Medium
Data ScientistDevelop a Restaurant-Recommendation Engine with Logistic Regression
Machine Learning
106
0
July 12, 2025
Meta
Medium
Data ScientistDetermine Features for Effective Hashtag Recommendations
Machine Learning
104
1
July 12, 2025
Snapchat
Medium
Data ScientistBuild Predictive Model for Product Metric: Steps Explained
Machine Learning
103
0
July 12, 2025
Amazon
Medium
Data ScientistExplain Decision-Tree Training and Clustering Algorithms
Machine Learning
80
0
August 4, 2025
Tesla
Hard
Machine Learning EngineerDesign RL reward for speed limits
Machine Learning
22
0
February 12, 2026
IBM
Easy
Data ScientistDesign a lead-scoring model
Machine Learning
25
0
January 17, 2026
Upstart
Hard
Data ScientistHow to Architect a Personalized Ads Serving System
Machine Learning
74
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.