CVS Health
Medium
Data ScientistBuild a leak-free sklearn churn pipeline
Machine Learning
7
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.
525Questions
115Companies
Easy 52 • Medium 261 • 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
Hard
Data ScientistExplain logistic regression vs forests and boosting
Machine Learning
7
0
October 13, 2025
Expedia
Hard
Data ScientistValidate and monitor ranking model end-to-end
Machine Learning
6
0
October 13, 2025
Amazon
Medium
Machine Learning EngineerExplain Layer Normalization in Transformers
Machine Learning
7
0
September 6, 2025
Microsoft
Easy
Data ScientistExplain SHAP and build an ML project
Machine Learning
5
0
February 9, 2026
Pinterest
Hard
Machine Learning EngineerImplement Naive Bayes classifier from scratch
Machine Learning
4
0
February 9, 2026
Point72
Hard
Data ScientistExplain and tune decision trees robustly
Machine Learning
3
0
October 13, 2025
Meta
Easy
Analytics EngineerDesign and evaluate an ads ranking algorithm
Machine Learning
7
0
February 15, 2026
Snapchat
Medium
Machine Learning EngineerExplain BatchNorm, optimizers, and L1/L2
Machine Learning
4
0
February 11, 2026
Reddit
Medium
Machine Learning EngineerAnalyze CTR Data and Train Model
Machine Learning
4
0
February 1, 2026
Uber
Medium
Data ScientistDesign a Ride-Hailing ETA System
Machine Learning
5
0
January 14, 2026
PayPal
Hard
Data ScientistExplain fraud types and evaluate a fraud model
Machine Learning
3
0
January 2, 2026
Citadel
Hard
Data ScientistDesign city home-price prediction system
Machine Learning
5
0
October 13, 2025
Instacart
Hard
Data ScientistImprove low R² without p‑hacking
Machine Learning
6
0
October 13, 2025
Boston Consulting Group
Medium
Data ScientistBuild a leak-free sklearn pipeline
Machine Learning
6
0
October 13, 2025
Amazon
Hard
Data ScientistBuild Accurate Energy Consumption Prediction Model for Utilities
Machine Learning
13
0
July 12, 2025
Uber
Medium
Data ScientistPredict driver acceptance
Machine Learning
4
0
March 22, 2026
Apple
Medium
Machine Learning EngineerExplain dataset size, generalization, and U-Net skips
Machine Learning
7
0
March 1, 2026
Snapchat
Medium
Machine Learning EngineerModel an ads ranking system
Machine Learning
6
0
February 12, 2026
Google
Hard
Data ScientistModel Shot Success by Location
Machine Learning
3
0
December 3, 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.