Waymo
Easy
Data ScientistHow predict vehicles’ turn direction at intersection?
Machine Learning
28
0
January 17, 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
LinkedIn
Easy
Data ScientistDesign a short-video recommender system
Machine Learning
23
0
February 1, 2026
Microsoft
Easy
Data ScientistExplain KNN and how to tune it
Machine Learning
26
0
January 17, 2026
OpenAI
Medium
Machine Learning EngineerFilter Bad Human Annotations
Machine Learning
23
0
April 3, 2026
PayPal
Medium
Data ScientistIdentify Unsupervised Techniques for Detecting Fraudulent Transactions
Machine Learning
132
0
August 4, 2025
Anthropic
Hard
Software EngineerImplement and analyze custom attention
Machine Learning
22
0
September 6, 2025
Google
Medium
Data ScientistBuild Model to Predict Customer Contract Renewal
Machine Learning
110
0
July 12, 2025
Voleon Group
Hard
Data ScientistDesign and diagnose a regression pipeline
Machine Learning
26
0
October 13, 2025
OpenAI
Hard
Machine Learning EngineerImplement Backprop for a Tiny Network
Machine Learning
21
0
April 3, 2026
Snapchat
Medium
Data ScientistBuild Predictive Model for Product Metric: Steps Explained
Machine Learning
103
0
July 12, 2025
Google
Medium
Data ScientistEngineer Features to Enhance Smartphone Battery Life Prediction
Machine Learning
99
0
July 12, 2025
Uber
Hard
Data ScientistOptimize Surge Notifications for Rideshare Drivers
Machine Learning
91
0
August 4, 2025
Coinbase
Hard
Data ScientistHow to Analyze and Model Behavioral Data Effectively?
Machine Learning
100
0
July 12, 2025
Scale AI
Easy
Machine Learning EngineerExplain LLM post-training methods and tradeoffs
Machine Learning
19
0
February 12, 2026
Meta
Medium
Data ScientistDevelop a Restaurant-Recommendation Engine with Logistic Regression
Machine Learning
105
0
July 12, 2025
Meta
Hard
Data ScientistIdentify Fake Accounts Using Machine Learning Techniques
Machine Learning
100
0
August 4, 2025
Google
Medium
Data ScientistCompare Logistic Regression and Random Forest in Limited Data Scenarios
Machine Learning
93
0
July 12, 2025
Meta
Medium
Data ScientistDetermine Features for Effective Hashtag Recommendations
Machine Learning
102
1
July 12, 2025
IBM
Easy
Data ScientistDesign a lead-scoring model
Machine Learning
24
0
January 17, 2026
Upstart
Medium
Data ScientistDesign a Regression Model for Robust Extrapolation Performance
Machine Learning
69
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.