TikTok
Medium
Data ScientistPredict User Churn with Effective Modeling Techniques
Machine Learning
1
0
August 4, 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
TikTok
Medium
Software EngineerExplain DPO and construct its training data
Machine Learning
1
0
December 8, 2025
Meta
Hard
Data ScientistHow would you design Shop-ad ranking?
Machine Learning
2
0
October 16, 2025
Meta
Hard
Data ScientistDesign bot detection and evaluate trade-offs
Machine Learning
1
0
October 13, 2025
Other
Medium
Data ScientistCompare trees, RF, and gradient boosting
Machine Learning
2
0
October 13, 2025
Capital One
Medium
Data ScientistExplain MSE vs MAE, AUC, and imbalance handling
Machine Learning
2
0
October 13, 2025
Thumbtack
Medium
Data ScientistForecast response-rate trends with backtesting
Machine Learning
1
0
October 13, 2025
TikTok
Medium
Data ScientistChoose linear regression or decision tree appropriately
Machine Learning
1
0
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain imbalance, metrics, bias-variance, Transformers vs. CNNs
Machine Learning
2
0
September 6, 2025
Amazon
Medium
Data ScientistDesign a Churn Model: Handle Missing Data and Justify
Machine Learning
1
0
August 4, 2025
First American
Medium
Data ScientistDescribe Building and Deploying a Machine Learning Model
Machine Learning
2
0
August 4, 2025
Meta
Medium
Data ScientistEvaluate Product-Ranking Algorithm with Precision and Recall Metrics
Machine Learning
2
0
August 4, 2025
Capital One
Medium
Data ScientistIdentify Risks and Improve Imputation Class Implementations
Machine Learning
1
0
August 4, 2025
Citibank
Medium
Data ScientistDiscuss logistic regression limitations for PD
Machine Learning
1
0
July 26, 2025
Bytedance
Hard
Data ScientistHow to deploy multimodal models?
Machine Learning
1
0
January 4, 2026
Meta
Medium
Data ScientistHow would you predict a car’s turning intention?
Machine Learning
2
0
November 24, 2025
Meta
Hard
Data ScientistReplace legacy ads model safely
Machine Learning
2
0
October 13, 2025
Capital One
Hard
Data ScientistBuild and validate a binary classifier
Machine Learning
2
0
October 13, 2025
TikTok
Medium
Data ScientistPersonalize Ad Delivery Using Machine Learning Techniques
Machine Learning
1
0
August 4, 2025
Meta
Hard
Data ScientistDesign a System to Recommend Local Restaurant Profiles
Machine Learning
1
0
August 4, 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.