TikTok
Hard
Data ScientistDesign Real-Time Credit Card Fraud Detection System
Machine Learning
16
0
July 12, 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.
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
Microsoft
Hard
Data ScientistCompare CNN/RNN/LSTM and implement K-means
Machine Learning
5
0
October 13, 2025
Google
Medium
Machine Learning EngineerExplain GRPO-style training for diffusion models
Machine Learning
4
0
December 8, 2025
DoorDash
Medium
Machine Learning EngineerHow would you target promotions to grow consumers?
Machine Learning
5
0
October 17, 2025
Uber
Hard
Data ScientistBuild and assess CTR prediction
Machine Learning
4
0
October 13, 2025
Amazon
Hard
Data ScientistDesign a Machine Learning Recommendation System Pipeline
Machine Learning
17
0
August 4, 2025
Meta
Medium
Data ScientistChoose Metrics for Evaluating Fake-User Classifier
Machine Learning
17
0
July 12, 2025
Amazon
Medium
Software EngineerExplain overfitting, regularization, and LLM techniques
Machine Learning
3
0
February 12, 2026
Microsoft
Medium
Machine Learning EngineerClean OCR data and build an LLM dataset
Machine Learning
3
0
February 11, 2026
Coinbase
Hard
Data ScientistBuild and evaluate a conversion prediction model
Machine Learning
3
0
October 13, 2025
Uber
Medium
Machine Learning EngineerExplain XGBoost depth, regularization, and dropout
Machine Learning
3
0
September 6, 2025
Intuit
Medium
Data ScientistEngineer and Impute ZIP Features
Machine Learning
4
0
February 23, 2026
Jane Street
Medium
Data ScientistAnalyze trading RFQ competitiveness data
Machine Learning
2
0
October 14, 2025
DoorDash
Hard
Data ScientistBuild a late-delivery risk model
Machine Learning
5
0
October 13, 2025
Amazon
Medium
Machine Learning EngineerExplain core ML concepts and diagnostics
Machine Learning
6
0
December 15, 2025
Amazon
Hard
Data ScientistDiagnose and fix underperforming ML model
Machine Learning
5
0
October 13, 2025
Lyft
Medium
Machine Learning EngineerExplain Transformer and Fine-Tuning Basics
Machine Learning
5
0
March 12, 2026
Netflix
Medium
Machine Learning EngineerExplain self-attention, LoRA, Adam vs SGD, ViT
Machine Learning
5
0
February 23, 2026
Upstart
Easy
Data ScientistDerive logistic regression objective and gradients
Machine Learning
2
0
October 13, 2025
DRW
Medium
Machine Learning EngineerExplain core ML and DL fundamentals
Machine Learning
6
0
July 31, 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.