Snapchat
Medium
Machine Learning EngineerExplain Core ML Concepts
Machine Learning
2
0
February 2, 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.
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
Filter and sort
Tubitv
Medium
Machine Learning EngineerExplain ML basics and recommender tuning
Machine Learning
1
0
January 28, 2026
Amazon
Hard
Data ScientistDesign fraud detection across channels with unknowns
Machine Learning
3
0
October 13, 2025
Amazon
Medium
Data ScientistDesign a Churn Model: Handle Missing Data and Justify
Machine Learning
1
0
August 4, 2025
Spokeo
Medium
Data ScientistUnderstand Bias-Variance Trade-off and Regularization Techniques
Machine Learning
2
0
August 4, 2025
Citibank
Medium
Data ScientistDiscuss logistic regression limitations for PD
Machine Learning
2
0
July 26, 2025
WeRide
Medium
Data ScientistEvaluate Guangzhou performance with limited data
Machine Learning
3
0
January 23, 2025
Bytedance
Hard
Data ScientistHow to deploy and tune multimodal models?
Machine Learning
3
0
January 10, 2026
Meta
Hard
Data ScientistDesign a hashtag recommender for News Feed
Machine Learning
3
0
October 13, 2025
TikTok
Medium
Data ScientistChoose linear regression or decision tree appropriately
Machine Learning
2
0
October 13, 2025
Ancestry
Medium
Software EngineerExplain learning paradigms, loss, and embeddings
Machine Learning
3
0
February 11, 2026
Reuters
Easy
Software EngineerDiscuss overfitting, contrastive learning, transformers
Machine Learning
2
0
November 14, 2025
CVS Health
Hard
Data ScientistHandle challenges in MMM/MMX
Machine Learning
1
0
October 13, 2025
Meta
Medium
Data ScientistEvaluate New Model's Performance Against Existing System
Machine Learning
3
0
August 4, 2025
Meta
Hard
Data ScientistDesign a restaurant recommender under cold start
Machine Learning
2
0
October 13, 2025
C3 AI
Easy
Data ScientistIdentify a classic bagging algorithm
Machine Learning
1
0
August 9, 2025
Spokeo
Hard
Data ScientistDesign Comprehensive Recommendation System for Spokeo Features
Machine Learning
2
0
August 4, 2025
Bnp
Easy
Data ScientistWhat are linear regression assumptions?
Machine Learning
2
0
January 5, 2026
Ansys
Medium
Software EngineerExplain Common Machine Learning Tradeoffs
Machine Learning
2
0
December 31, 2025
Other
Hard
Data ScientistExplain OS usage gap via trees
Machine Learning
2
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.