Uber
Medium
Data ScientistPredict driver acceptance
Machine Learning
1
0
March 22, 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.
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
Dandy
Medium
Machine Learning EngineerExplain ML fundamentals (activations, CV, vision, sorting)
Machine Learning
2
0
January 17, 2026
TikTok
Medium
Software EngineerExplain RL policy types and modern policy gradients
Machine Learning
2
0
January 11, 2026
Point72
Hard
Data ScientistHow would you explain PCA and SHAP?
Machine Learning
1
0
October 24, 2025
OneMain Financial
Medium
Data ScientistChoose evaluation metrics for imbalanced risk model
Machine Learning
2
0
October 13, 2025
Other
Hard
Data ScientistExtract companies from noisy text
Machine Learning
1
0
October 13, 2025
CVS Health
Hard
Data ScientistBuild an uplift model for targeting
Machine Learning
2
0
October 13, 2025
Snowflake
Hard
Data ScientistDesign and validate a cost-sensitive classifier
Machine Learning
2
0
October 13, 2025
TikTok
Medium
Data ScientistCompare bagging vs boosting on imbalanced data
Machine Learning
2
0
October 13, 2025
Walmart Labs
Medium
Data ScientistEvaluate K-Fold Cross-Validation for Model Selection
Machine Learning
1
0
August 4, 2025
Citibank
Medium
Data ScientistIdentify top exposures and mitigate
Machine Learning
1
0
July 26, 2025
Amazon
Hard
Machine Learning EngineerExplain LLM fundamentals and trade-offs
Machine Learning
2
0
July 17, 2025
Amazon
Medium
Software EngineerExplain core ML fundamentals
Machine Learning
3
0
July 15, 2025
Meta
Easy
Analytics EngineerDesign and evaluate an ads ranking algorithm
Machine Learning
2
0
February 15, 2026
Intuit
Medium
Data ScientistEngineer ZIP Features and Handle Missingness
Machine Learning
1
0
February 10, 2026
Microsoft
Medium
Data ScientistHow would you build and evaluate a classifier?
Machine Learning
1
0
January 16, 2026
Apple
Medium
Machine Learning EngineerExplain classification lifecycle and CTR modeling
Machine Learning
1
0
December 17, 2025
Amazon
Easy
Machine Learning EngineerDescribe overfitting and L1/L2 regularization
Machine Learning
1
0
December 8, 2025
Hudson
Medium
Data ScientistDerive expected inversions and mean distribution
Machine Learning
2
0
November 28, 2025
Amazon
Medium
Machine Learning EngineerExplain surprisal and its units
Machine Learning
2
0
November 20, 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.