Snapchat
Medium
Machine Learning EngineerExplain CLIP, contrastive losses, and retrieval limits
Machine Learning
5
0
February 3, 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
Filter and sort
Amazon
Medium
Data ScientistOptimize Feature Selection and Handling in Machine Learning Models
Machine Learning
18
0
July 12, 2025
Amazon
Hard
Data ScientistDiagnose and fix underperforming ML model
Machine Learning
6
0
October 13, 2025
TikTok
Medium
Data ScientistPredict Customer Churn with Machine Learning Workflow
Machine Learning
18
0
July 12, 2025
Spotify
Medium
Machine Learning EngineerBuild Premium User Propensity Model
Machine Learning
4
0
March 4, 2026
Coinbase
Medium
Machine Learning EngineerBuild a baseline classification model from messy data
Machine Learning
6
0
December 4, 2025
Morgan Stanley
Hard
Data ScientistExplain futures pricing and linear regression basics
Machine Learning
5
0
October 20, 2025
Meta
Medium
Data ScientistChoose Metrics for Evaluating Fake-User Classifier
Machine Learning
18
0
July 12, 2025
LinkedIn
Medium
Data ScientistExplain variance reduction in random forests
Machine Learning
8
0
February 19, 2026
TikTok
Medium
Machine Learning EngineerExplain FlashAttention, KV cache, and RoPE
Machine Learning
3
0
January 22, 2026
Amazon
Medium
Machine Learning EngineerHandle cold start, dropout, and training stability
Machine Learning
6
0
January 6, 2026
Amazon
Medium
Machine Learning EngineerExplain Transformers and MoE in LLMs
Machine Learning
7
0
December 8, 2025
Google
Hard
Data ScientistPredict and act on contract renewal risk
Machine Learning
7
0
October 13, 2025
Instacart
Hard
Data ScientistContrast Lasso vs Ridge trade‑offs
Machine Learning
6
0
October 13, 2025
Amazon
Medium
Data ScientistCompare Regularization Techniques and Their Use Cases
Machine Learning
13
0
July 12, 2025
Uber
None
Data ScientistImplement Streaming Clustering for Numbers
Machine Learning
7
0
April 10, 2026
Snapchat
Medium
Machine Learning EngineerExplain core ML fundamentals and tradeoffs
Machine Learning
5
0
January 10, 2026
Pinterest
Hard
Machine Learning EngineerExplain overfitting and how to prevent it
Machine Learning
7
0
December 13, 2025
Uber
Hard
Data ScientistSelect the better $5 promo-targeting model
Machine Learning
7
0
October 13, 2025
Two Sigma
Medium
Data ScientistAnalyze Temperatures and Update Regression
Machine Learning
5
0
April 21, 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.