EvenUp
Medium
Data ScientistBuild a model to predict wine quality
Machine Learning
5
0
September 24, 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
Uber
Easy
Data ScientistHow would you design an ETA prediction system?
Machine Learning
5
0
February 6, 2026
EliseAI
Medium
Software EngineerExplain prompt engineering strategies for chatbots
Machine Learning
7
0
September 6, 2025
Meta
Hard
Data ScientistIdentify Features for Fake News Detection on Facebook
Machine Learning
28
0
August 4, 2025
Google
Hard
Data ScientistFind companies similar to a given client
Machine Learning
6
0
October 13, 2025
Citadel
Hard
Data ScientistDiagnose outliers and influence in linear regression
Machine Learning
3
0
October 13, 2025
Citadel
Hard
Machine Learning EngineerMake a hard MoE router differentiable
Machine Learning
7
0
July 15, 2025
Applied Intuition
Medium
Machine Learning EngineerImplement and explain positional encoding
Machine Learning
5
0
September 6, 2025
Meta
Medium
Data ScientistDesign and Validate Initial Restaurant Recommendation Model
Machine Learning
26
0
July 12, 2025
Bank of America
Medium
Data ScientistExplain core probability and ML statistics concepts
Machine Learning
6
0
February 11, 2026
Google
Medium
Data ScientistExplain Linear Regression to Non-Technical Stakeholders
Machine Learning
15
0
July 12, 2025
Pinterest
Hard
Machine Learning EngineerExplain overfitting, underfitting, and regularization
Machine Learning
7
0
March 1, 2026
DoorDash
Hard
Data ScientistDesign and evaluate an uplift model
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
Atlassian
Medium
Data ScientistMinimize L1 Distance with k Cluster Centers in Array
Machine Learning
14
0
July 12, 2025
Amazon
Medium
Data ScientistOptimize Predictive Analytics: Feature Engineering to Model Evaluation
Machine Learning
15
0
July 12, 2025
TikTok
Medium
Machine Learning EngineerExplain overfitting, dropout, normalization, RL post-training
Machine Learning
6
0
February 12, 2026
Amazon
Medium
Machine Learning EngineerImplement SGD for linear regression and derive gradients
Machine Learning
5
0
December 15, 2025
Perplexity
Medium
Machine Learning EngineerTrain and improve a scikit-learn binary classifier
Machine Learning
5
0
December 15, 2025
Voleon
Hard
Machine Learning EngineerExplain ML and statistical modeling
Machine Learning
4
0
February 15, 2026
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.