Coinbase
Hard
Machine Learning EngineerExplain precision/recall and compute NN output
Machine Learning
3
0
February 1, 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
Pinterest
Hard
Machine Learning EngineerExplain overfitting and how to prevent it
Machine Learning
4
0
December 13, 2025
Meta
Easy
Data ScientistDesign an ad recommendation ranking approach
Machine Learning
4
0
December 6, 2025
Google
Medium
Machine Learning EngineerExplain modeling challenges and fixes
Machine Learning
3
0
September 6, 2025
DRW
Medium
Machine Learning EngineerExplain core ML concepts
Machine Learning
2
0
August 4, 2025
Amazon
Medium
Machine Learning EngineerDesign a search relevance prediction approach
Machine Learning
3
0
January 6, 2026
Morgan Stanley
Hard
Data ScientistExplain futures pricing and linear regression basics
Machine Learning
3
0
October 20, 2025
Uber
Hard
Data ScientistBuild and deploy an uplift targeting model
Machine Learning
2
0
October 13, 2025
Boston Consulting Group
Easy
Data ScientistInterpret AUC Values and Handle Class Imbalance Techniques
Machine Learning
4
0
August 4, 2025
Amazon
Medium
Machine Learning EngineerExplain core ML fundamentals
Machine Learning
3
0
July 17, 2025
Transunion
Medium
Data ScientistExplain Feature, Model, and Validation Choices
Machine Learning
2
0
April 11, 2026
Natoora
Medium
Data AnalystJustify Using LLMs for Reporting
Machine Learning
2
0
March 4, 2026
Snapchat
Medium
Machine Learning EngineerExplain BatchNorm, optimizers, and L1/L2
Machine Learning
2
0
February 11, 2026
Microsoft
Hard
Data ScientistDesign a model for imbalanced conversions
Machine Learning
4
0
October 13, 2025
Two Sigma
Easy
Data ScientistHow to forecast bike dock demand
Machine Learning
4
0
February 13, 2026
Meta
Easy
Data ScientistHow would you design a Shop Ads ranking algorithm?
Machine Learning
2
0
February 12, 2026
Uber
Medium
Data ScientistDesign a Ride-Hailing ETA System
Machine Learning
3
0
January 14, 2026
Amazon
Hard
Data ScientistDesign end-to-end regression for energy demand
Machine Learning
3
0
October 13, 2025
Capital One
Hard
Data ScientistModel flight delays with EDA and explanation
Machine Learning
3
0
October 13, 2025
NVIDIA
Hard
Data ScientistAnalyze overfitting, DenseNet, preprocessing, and cross-validation
Machine Learning
4
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.