Upstart
Medium
Data ScientistLeverage Existing Model for Low Credit Score Applicants
Machine Learning
23
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
Tesla
Hard
Machine Learning EngineerDesign RL reward for speed limits
Machine Learning
11
0
February 12, 2026
Apple
Hard
Software EngineerImplement multi-head self-attention correctly
Machine Learning
7
0
September 6, 2025
OneMain Financial
Medium
Data ScientistHandle Missing Values and Outliers in Machine Learning
Machine Learning
45
0
July 12, 2025
Amazon
Medium
Data ScientistCompare RNNs and Transformers for Long-Sequence Text Classification
Machine Learning
43
0
August 4, 2025
LinkedIn
Medium
Machine Learning EngineerAnswer practical ML foundations questions
Machine Learning
11
0
February 18, 2026
Scale AI
Medium
Machine Learning EngineerImplement universal adversarial attack on GPT-2
Machine Learning
8
0
July 8, 2025
Meta
Medium
Data ScientistEmploy Collaborative Filtering for Personalized Recommendation Lists
Machine Learning
37
0
July 12, 2025
NVIDIA
Medium
Software EngineerExplain Transformers and QKV matrices
Machine Learning
7
0
July 15, 2025
Meta
Medium
Data ScientistIdentify Fake Accounts Using Machine Learning Techniques
Machine Learning
26
0
August 4, 2025
Upstart
Hard
Data ScientistDesign a Real-Time Personalized Ad Selection System
Machine Learning
5
0
August 4, 2025
Capital One
Hard
Data ScientistDesign a robust fraud detection system
Machine Learning
5
0
October 13, 2025
Google
Hard
Machine Learning EngineerExplain transformer architecture and variants
Machine Learning
9
0
September 6, 2025
Shopify
Medium
Machine Learning EngineerBuild model to predict package delivery time
Machine Learning
8
0
February 18, 2026
Microsoft
Easy
Data ScientistCompute and plot a precision–recall curve
Machine Learning
5
0
January 17, 2026
Uber
Hard
Data ScientistDesign ETA prediction for Uber rides
Machine Learning
7
0
October 13, 2025
DoorDash
Hard
Data ScientistBuild ETA prediction and simulate impact
Machine Learning
6
0
October 13, 2025
Reddit
Medium
Machine Learning EngineerBuild and evaluate click prediction models
Machine Learning
6
0
September 6, 2025
Point72
Easy
Data ScientistExplain project details, PCA, and SHAP
Machine Learning
5
0
January 17, 2026
Amazon
Hard
Data ScientistDesign an end-to-end spam detection system
Machine Learning
8
0
October 13, 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.