Amazon
Hard
Data ScientistChoose Models for Imbalanced Data and Time-Series Forecasting
Machine Learning
53
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
Nextdoor
Medium
Machine Learning EngineerBuild a model using only pandas/numpy
Machine Learning
16
0
March 1, 2026
OpenAI
Medium
Machine Learning EngineerDebug Transformer and Add KV Cache
Machine Learning
13
0
February 1, 2026
Amazon
Medium
Data ScientistDesign an Automated Home-Price Valuation Model
Machine Learning
61
0
August 4, 2025
Apple
Medium
Data ScientistConstruct a Churn-Prediction Pipeline Using Scikit-Learn
Machine Learning
33
0
July 12, 2025
LinkedIn
Medium
Machine Learning EngineerExplain activations, losses, and Adam
Machine Learning
13
0
February 11, 2026
Amazon
Medium
Data ScientistHandle Missing Values and Choose ML Algorithms Wisely
Machine Learning
58
0
July 12, 2025
Lyft
Hard
Data ScientistDesign Dynamic Pricing System for Lyft: Key Features & Models
Machine Learning
20
0
August 4, 2025
Google
Medium
Machine Learning EngineerCompare NLP tokenization and LLM recommendations
Machine Learning
12
0
February 8, 2026
DoorDash
Hard
Data ScientistDesign a Homepage Store Recommender
Machine Learning
10
0
March 15, 2026
Amazon
Hard
Machine Learning EngineerExplain NLP/RL concepts used in LLM agents
Machine Learning
11
0
February 9, 2026
Amazon
Medium
Data ScientistDiagnose Bias–Variance Trade-off in Supervised Learning
Machine Learning
50
0
August 4, 2025
Google
Hard
Data ScientistDesign a battery-life predictor and cold-start strategy
Machine Learning
8
0
October 13, 2025
Uber
Medium
Data ScientistBuild cold-start restaurant ratings
Machine Learning
12
0
April 6, 2026
Amazon
Hard
Machine Learning EngineerExplain key ML theory and techniques
Machine Learning
23
0
August 4, 2025
Upstart
Hard
Data ScientistAddress Missing Income Bracket in California Housing Data
Machine Learning
28
0
August 4, 2025
TikTok
Medium
Machine Learning EngineerWrite self-attention and cross-entropy pseudocode
Machine Learning
7
0
February 12, 2026
TikTok
Easy
Data ScientistDesign multimodal deployment under compute limits
Machine Learning
10
0
February 17, 2026
Google
Medium
Software EngineerExplain LLM fine-tuning and generative models
Machine Learning
11
0
February 12, 2026
Uber
Medium
Machine Learning EngineerImplement 1D convex minimization in Python
Machine Learning
10
0
September 6, 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.