Meta
Medium
Data ScientistIdentify Fake Accounts Using Machine Learning Techniques
Machine Learning
27
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.
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
Medium
Data ScientistBuild Classifier: Evaluate with AUROC for Imbalanced Data
Machine Learning
29
0
July 12, 2025
Uber
Easy
Data ScientistHow would you design an ETA prediction system?
Machine Learning
8
0
February 6, 2026
Jane Street
Medium
Data ScientistDesign a Real-vs-Fake DNA Classifier
Machine Learning
5
0
January 27, 2026
Airbnb
Medium
Machine Learning EngineerDesign photo and listing quality models
Machine Learning
8
0
January 5, 2026
Apple
Medium
Machine Learning EngineerAnalyze vision model failures
Machine Learning
8
0
March 1, 2026
Citadel
Hard
Data ScientistDiagnose outliers and influence in linear regression
Machine Learning
5
0
October 13, 2025
Pinterest
Hard
Machine Learning EngineerExplain overfitting, underfitting, and regularization
Machine Learning
11
0
March 1, 2026
LinkedIn
Easy
Machine Learning EngineerExplain overfitting vs underfitting and fixes
Machine Learning
7
0
February 8, 2026
Meta
Hard
Data ScientistDesign a Restaurant Recommendation System for Food Apps
Machine Learning
31
0
July 12, 2025
Lyft
Medium
Machine Learning EngineerExplain Transformer and Fine-Tuning Basics
Machine Learning
9
0
March 12, 2026
Google
Medium
Machine Learning EngineerExplain modeling challenges and fixes
Machine Learning
9
0
September 6, 2025
Capital One
Medium
Data ScientistHow would you design delay and watchlist models?
Machine Learning
8
0
January 30, 2026
EvenUp
Medium
Data ScientistBuild a model to predict wine quality
Machine Learning
7
0
September 24, 2025
Amazon
Medium
Data ScientistOptimize XGBoost for Predicting Marketing Outcomes
Machine Learning
32
0
August 4, 2025
Netflix
Medium
Machine Learning EngineerExplain self-attention, LoRA, Adam vs SGD, ViT
Machine Learning
9
0
February 23, 2026
Two Sigma
Nan
Data ScientistDesign features for house price prediction
Machine Learning
10
0
January 22, 2026
Microsoft
Medium
Machine Learning EngineerCompare preference alignment methods for LLMs
Machine Learning
10
0
January 6, 2026
TikTok
Medium
Machine Learning EngineerExplain your VLM project end-to-end
Machine Learning
6
0
December 15, 2025
Microsoft
Hard
Data ScientistCompare CNN/RNN/LSTM and implement K-means
Machine Learning
9
0
October 13, 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.