Walmart Labs
Easy
Machine Learning EngineerDesign an LLM agent with RAG and tools
Machine Learning
1
0
December 7, 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
Gts
Medium
Data ScientistCompute value of card guessing game
Machine Learning
0
0
September 18, 2025
Meta
Medium
Data ScientistWhat features and feature selection would you use?
Machine Learning
1
0
August 10, 2025
Boston Consulting Group
Medium
Data ScientistImprove Model Generalization with Cross-Validation and Feature Engineering
Machine Learning
0
0
August 4, 2025
Meta
Hard
Data ScientistDesign Machine Learning Model for Facebook Groups Post Ranking
Machine Learning
0
0
August 4, 2025
Snapchat
Medium
Machine Learning EngineerExplain Core ML Concepts
Machine Learning
0
0
June 28, 2025
Circle
Medium
Data ScientistPredict Future Car Sale Prices
Machine Learning
0
0
January 19, 2026
Other
Hard
Data ScientistExplain SVM kernels and complexity
Machine Learning
0
0
October 13, 2025
WeRide
Medium
Data ScientistEvaluate Guangzhou performance with limited data
Machine Learning
1
0
January 23, 2025
Meta
Hard
Data ScientistOptimize IG Shopping ranking with multiple objectives
Machine Learning
0
0
October 13, 2025
WeRide
Medium
Data ScientistEvaluate New-City Performance with Little Data
Machine Learning
0
0
January 4, 2025
Intersystems
Medium
Software EngineerHow would you evaluate an AI feature?
Machine Learning
0
0
February 12, 2026
Amazon
Medium
Machine Learning EngineerCompare float types and design ablation
Machine Learning
0
0
December 8, 2025
Meta
Hard
Data ScientistHow to design Shop ad ranking
Machine Learning
0
0
October 26, 2025
Meta
Hard
Data ScientistDesign a restaurant recommender under constraints
Machine Learning
0
0
October 13, 2025
LinkedIn
Medium
Machine Learning EngineerExplain Core ML Fundamentals
Machine Learning
0
0
May 3, 2025
Cadence
Medium
Machine Learning EngineerExplain Transformer Positional Encoding
Machine Learning
0
0
January 5, 2026
Ansys
Medium
Software EngineerExplain Common Machine Learning Tradeoffs
Machine Learning
0
0
December 31, 2025
Point72
Medium
Machine Learning EngineerDesign Features for Residual Volatility
Machine Learning
0
0
April 14, 2026
Snapchat
Medium
Machine Learning EngineerExplain Core ML Concepts
Machine Learning
0
0
February 2, 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.