Amazon
Medium
Data ScientistExplain Decision-Tree Training and Clustering Algorithms
Machine Learning
78
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.
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
Upstart
Hard
Data ScientistHow to Architect a Personalized Ads Serving System
Machine Learning
72
0
August 4, 2025
Citadel
Hard
Data ScientistDesign Framework for Robust House-Price Prediction Model
Machine Learning
90
0
July 12, 2025
Anthropic
Medium
Machine Learning EngineerExplain batch inference design
Machine Learning
20
0
February 27, 2026
Amazon
Hard
Data ScientistEvaluate Ensemble Models for Bias-Variance, Speed, and Interpretability
Machine Learning
83
0
August 4, 2025
Snapchat
Hard
Data ScientistOptimize Churn Prediction: Feature Engineering and Model Selection
Machine Learning
86
0
August 4, 2025
Jane Street
Hard
Machine Learning EngineerBuild a time-series forecasting model
Machine Learning
13
0
August 1, 2025
Google
Medium
Data ScientistIdentify and Fix Predictive Model Performance Gaps
Machine Learning
79
0
July 12, 2025
Waymo
Easy
Data ScientistImplement K-means and handle train-inference mismatch
Machine Learning
10
0
December 6, 2025
Uber
Medium
Machine Learning EngineerImplement convex minimization on an interval
Machine Learning
10
0
August 10, 2025
Amazon
Hard
Data ScientistDesign an ML Model for Interview Recommendation Pipeline
Machine Learning
69
0
August 4, 2025
OpenAI
Medium
Machine Learning EngineerDebug a Broken Transformer
Machine Learning
16
0
March 3, 2026
Two Sigma
Hard
Data ScientistPredict Bike Dock Demand
Machine Learning
18
0
March 18, 2026
Voleon Group
Hard
Data ScientistBuild a regularized regression pipeline
Machine Learning
7
0
October 13, 2025
Meta
Medium
Data ScientistEvaluate and Experiment with Harmful Content Detection Model
Machine Learning
71
0
August 4, 2025
Lyft
Hard
Data ScientistDevelop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors
Machine Learning
29
0
July 12, 2025
Amazon
Medium
Data ScientistOptimize Email Strategy for New Prime Video Series Launch
Machine Learning
68
0
July 12, 2025
Scale AI
Hard
Machine Learning EngineerExplain Transformers, attention, decoding, RL, and evaluation
Machine Learning
13
0
September 6, 2025
Upstart
Medium
Data ScientistDesign Push-Notification System for Airport Surge Pricing
Machine Learning
44
0
August 4, 2025
Capital One
Medium
Data ScientistEvaluate OutlierHandler Class for Code Quality and Testing
Machine Learning
62
0
July 12, 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.