Tower
Hard
Data ScientistExplain Why Constant Neural-Network Initialization Fails
Machine Learning
1
0
July 26, 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.
594Questions
127Companies
Easy 63 • Medium 314 • Hard 217Difficulty 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
Visa
Medium
Machine Learning EngineerAnswer RAG, Transformer, And Fraud Metric Fundamentals
Machine Learning
1
0
April 7, 2026
Tower
Hard
Data ScientistPredict Five-Minute Returns from Order-Book Data
Machine Learning
1
0
July 26, 2026
Etsy
Easy
Machine Learning EngineerExplain Graph Recommendations and Vision-Language Model Training Trade-offs
Machine Learning
1
0
June 13, 2026
Point72
Medium
Data ScientistExplain Ensemble Learning and Its Main Families
Machine Learning
1
0
December 14, 2025
Gsa
Medium
Data ScientistCritique a CNN-Based Bridge Safety Proposal
Machine Learning
1
0
May 17, 2026
Capital One
Medium
Data ScientistResolve a Mismatch Between Regression and Classification Metrics
Machine Learning
1
0
August 20, 2026
C3 AI
Hard
Data ScientistCompare Transformers and RNNs and Derive Attention Complexity
Machine Learning
1
0
August 21, 2026
C3 AI
Hard
Data ScientistKeep Gradient Descent from Stalling at Poor Solutions
Machine Learning
0
0
August 21, 2026
C3 AI
Hard
Data ScientistCompare PCA and Autoencoders Under the Curse of Dimensionality
Machine Learning
0
0
August 21, 2026
Capital One
Medium
Data ScientistApply Leakage-Safe Imputation and Ordinal Encoding
Machine Learning
0
0
August 20, 2026
C3 AI
Hard
Data ScientistConnect Bias-Variance Trade-offs to Bagging and Boosting
Machine Learning
0
0
August 21, 2026
C3 AI
Medium
Data ScientistExplain RNN Applications Beyond Time-Series Forecasting
Machine Learning
0
0
August 9, 2026
C3 AI
Medium
Data ScientistChoose Tokenization and Dense or Sparse Retrieval
Machine Learning
0
0
August 9, 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.