Amazon
Hard
Machine Learning EngineerExplain LLM fundamentals and trade-offs
Machine Learning
5
0
July 17, 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
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Natoora
Medium
Data AnalystWhy use LLMs for daily summaries?
Machine Learning
2
0
February 11, 2026
Two Sigma
Easy
Data ScientistDesign a house-price prediction model
Machine Learning
5
0
December 15, 2025
Roblox
Hard
Data ScientistFit logistic regression and return top features
Machine Learning
2
0
November 23, 2025
Pinterest
Hard
Data ScientistExplain your ML project end-to-end
Machine Learning
3
0
October 13, 2025
Boston Consulting Group
Medium
Data ScientistImprove Model Generalization with Cross-Validation and Feature Engineering
Machine Learning
2
0
August 4, 2025
PayPal
Hard
Data ScientistExplain unsupervised fraud and evaluation
Machine Learning
2
0
July 31, 2025
Microsoft
Medium
Software EngineerExplain normalization, regularization, CTR, imbalance handling
Machine Learning
3
0
February 12, 2026
Amazon
Easy
Machine Learning EngineerCompare decision trees and random forests
Machine Learning
4
0
December 8, 2025
OneMain Financial
Easy
Data ScientistExplain decision trees and tree ensembles
Machine Learning
5
0
December 1, 2025
Roblox
Hard
Data ScientistDesign leakage-free predictive maintenance pipeline
Machine Learning
5
0
October 13, 2025
Amazon
Hard
Data ScientistBuild a package-allocation model for couriers
Machine Learning
3
0
October 13, 2025
Roblox
Easy
Data ScientistRank features using logistic regression coefficients
Machine Learning
5
0
October 3, 2025
Autodesk
Medium
Machine Learning EngineerExplain 3D geometry data
Machine Learning
5
0
July 17, 2025
Gusto
Medium
Data ScientistBuild a Churn Prediction Model
Machine Learning
4
0
May 29, 2026
Millennium
Medium
Software EngineerHow would you model stock price prediction?
Machine Learning
3
0
February 12, 2026
Meta
Medium
Software EngineerExplain key ML metrics and techniques
Machine Learning
4
0
December 8, 2025
Microsoft
Easy
Data ScientistExplain KNN and PCA and key tradeoffs
Machine Learning
3
0
November 24, 2025
Uber
Hard
Data ScientistBuild and deploy an uplift targeting model
Machine Learning
3
0
October 13, 2025
Google
Hard
Data ScientistExplain a favorite model end-to-end
Machine Learning
3
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.