Hudson
Hard
Software EngineerBuild a Heart Disease Baseline
Machine Learning
3
0
March 13, 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.
525Questions
115Companies
Easy 52 • Medium 261 • Hard 198Difficulty 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
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Snapchat
Hard
Machine Learning EngineerExplain LLM tuning and transformer basics
Machine Learning
5
0
January 30, 2026
Circle
Medium
Data ScientistPredict Future Car Sale Prices
Machine Learning
4
0
January 19, 2026
Amazon
Easy
Machine Learning EngineerDescribe overfitting and L1/L2 regularization
Machine Learning
2
0
December 8, 2025
Meta
Medium
Data ScientistDesign an ad recommendation and ranking system
Machine Learning
5
0
October 20, 2025
Boston Consulting Group
Medium
Data ScientistDesign and sample for credit default prediction
Machine Learning
3
0
October 13, 2025
Microsoft
Hard
Data ScientistDesign a model for imbalanced conversions
Machine Learning
6
0
October 13, 2025
Nextdoor
Hard
Software EngineerBuild and evaluate a Colab classification model
Machine Learning
6
0
July 31, 2025
Meta
Hard
Machine Learning EngineerImplement 1NN Embeddings and Forward Pass
Machine Learning
4
0
May 19, 2026
Transunion
Medium
Data ScientistExplain Feature, Model, and Validation Choices
Machine Learning
3
0
April 11, 2026
DoorDash
Hard
Data ScientistDesign a Low-Latency Store Recommender
Machine Learning
5
0
January 13, 2026
Meta
Hard
Data ScientistDeploy multi-armed bandits safely
Machine Learning
5
0
October 13, 2025
Roku
Medium
Data ScientistExplain and quantify bias-variance tradeoff
Machine Learning
4
0
October 13, 2025
NVIDIA
Hard
Data ScientistAnalyze overfitting, DenseNet, preprocessing, and cross-validation
Machine Learning
6
0
October 13, 2025
Coinbase
Hard
Machine Learning EngineerExplain precision/recall and compute NN output
Machine Learning
4
0
February 1, 2026
Atlassian
Medium
Data ScientistTrain and evaluate logistic model with regularization
Machine Learning
3
0
October 13, 2025
Apple
Hard
Data ScientistImplement random forest with OOB and imbalance
Machine Learning
4
0
October 13, 2025
Amazon
Hard
Data ScientistDerive and compare core ML and RL methods
Machine Learning
6
0
October 13, 2025
LinkedIn
Medium
Data ScientistTrain with imbalanced sampled data
Machine Learning
5
0
September 5, 2025
Uber
Medium
Data ScientistHow would you build UberEats ranking?
Machine Learning
6
0
January 22, 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.