Citadel
Medium
Data ScientistBuild a baseline linear regression pipeline
Machine Learning
3
0
August 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.
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
Microsoft
Medium
Software EngineerExplain normalization, regularization, CTR, imbalance handling
Machine Learning
2
0
February 12, 2026
Google
Medium
Software EngineerBuild a bigram next-word predictor with weighted sampling
Machine Learning
2
0
January 11, 2026
Cohere
Medium
Machine Learning EngineerExplain LLM training, RL, and evaluation
Machine Learning
2
0
December 25, 2025
CVS Health
Medium
Data ScientistBuild a leak-free sklearn churn pipeline
Machine Learning
3
0
October 13, 2025
Google
Hard
Data ScientistExplain logistic regression vs forests and boosting
Machine Learning
3
0
October 13, 2025
Google
Hard
Data ScientistDiagnose and fix flawed model fit
Machine Learning
3
0
October 13, 2025
Boston Consulting Group
Hard
Data ScientistReduce overfitting under constraints
Machine Learning
2
0
October 13, 2025
LinkedIn
Medium
Data ScientistTrain with imbalanced sampled data
Machine Learning
3
0
September 5, 2025
Amazon
Medium
Data ScientistExplain K-Fold Cross-Validation and Its Trade-Offs
Machine Learning
3
0
August 4, 2025
Pinterest
Hard
Machine Learning EngineerImplement Naive Bayes classifier from scratch
Machine Learning
2
0
February 9, 2026
Snapchat
Medium
Machine Learning EngineerExplain CLIP, contrastive losses, and retrieval limits
Machine Learning
2
0
February 3, 2026
TikTok
Medium
Machine Learning EngineerExplain FlashAttention, KV cache, and RoPE
Machine Learning
1
0
January 22, 2026
Uber
Medium
Data ScientistHow would you build UberEats ranking?
Machine Learning
4
0
January 22, 2026
Google
Hard
Data ScientistModel Soccer Shot Conversion
Machine Learning
2
0
December 29, 2025
Pinterest
Hard
Data ScientistExplain your ML project end-to-end
Machine Learning
2
0
October 13, 2025
Citibank
Medium
Data ScientistDiagnose and fix linear regression assumption breaks
Machine Learning
3
0
October 13, 2025
CVS Health
Medium
Data ScientistTune classifier and compute key metrics
Machine Learning
3
0
October 13, 2025
PayPal
Hard
Data ScientistBuild a real-time ATO model
Machine Learning
3
0
October 13, 2025
Spotify
Medium
Machine Learning EngineerBuild Premium User Propensity Model
Machine Learning
2
0
March 4, 2026
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.