Amazon
Easy
Machine Learning EngineerExplain the bias–variance trade-off
Machine Learning
3
0
December 8, 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
Filter and sort
Other
Hard
Data ScientistDetect clickbait without labels, then supervise
Machine Learning
3
0
October 13, 2025
Microsoft
Medium
Software EngineerCluster city name variants into canonical entities
Machine Learning
4
0
September 6, 2025
Meta
Medium
Data ScientistIdentify Algorithms for Detecting Malicious Duplicated Content
Machine Learning
4
0
August 4, 2025
Amazon
Hard
Data ScientistEvaluate RAG System Accuracy and Cost Control Strategies
Machine Learning
2
0
August 4, 2025
Meta
Medium
Data ScientistHow would you predict a car’s turning intention?
Machine Learning
4
0
November 24, 2025
Meta
Hard
Data ScientistReplace legacy ads model safely
Machine Learning
4
0
October 13, 2025
Other
Medium
Data ScientistEvaluate and select K in K-means
Machine Learning
2
0
October 13, 2025
Twitch
Hard
Data ScientistDesign real-time live-stream recommendations
Machine Learning
3
0
October 13, 2025
TikTok
Hard
Data ScientistEstimate heterogeneous treatment effects with causal ML
Machine Learning
3
0
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain Logistic Regression Fundamentals
Machine Learning
2
0
September 6, 2025
Bytedance
Easy
Data ScientistExplain train-test generalization gap
Machine Learning
3
0
March 9, 2026
Imc
Medium
Data ScientistExplain linear regression and Transformer fundamentals
Machine Learning
3
0
January 14, 2026
Apple
Medium
Machine Learning EngineerExplain Vision Encoders and LLM Bottlenecks
Machine Learning
3
0
November 11, 2025
Stripe
Hard
Data ScientistDesign a target‑user prediction system
Machine Learning
2
0
October 13, 2025
Other
Hard
Data ScientistDesign a hybrid marketplace fraud system
Machine Learning
2
0
October 13, 2025
Amazon
Medium
Data ScientistExplain Overfitting and Underfitting in Machine Learning
Machine Learning
3
0
August 4, 2025
NVIDIA
Medium
Software EngineerCompare deep learning framework trends
Machine Learning
4
0
July 31, 2025
Citibank
Medium
Data ScientistIdentify top exposures and mitigate
Machine Learning
1
0
July 26, 2025
C3 AI
Medium
Data ScientistExplain key ML/stats concepts
Machine Learning
1
0
July 25, 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.