Meta
Medium
Data ScientistBuild a model to infer home vs office vs public
Machine Learning
1
0
October 13, 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.
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
Meta
Medium
Data ScientistDetect and Reduce Spammy Friend Requests Effectively
Machine Learning
1
0
August 4, 2025
Stripe
Medium
Machine Learning EngineerNormalize targets for multitask regression
Machine Learning
1
0
April 18, 2026
Walmart Labs
Easy
Machine Learning EngineerDesign an LLM agent with RAG and tools
Machine Learning
1
0
December 7, 2025
Meta
Hard
Data ScientistHow to design Shop ad ranking
Machine Learning
1
0
October 26, 2025
Yelp
Hard
Data ScientistDesign a Cold-Start-Aware Recommender
Machine Learning
1
0
October 13, 2025
Intersystems
Medium
Software EngineerHow would you evaluate an AI feature?
Machine Learning
1
0
February 12, 2026
Bytedance
Medium
Machine Learning EngineerCompute Sentence Similarity
Machine Learning
1
0
January 7, 2026
Other
Hard
Data ScientistExplain SVM kernels and complexity
Machine Learning
1
0
October 13, 2025
Qube
Hard
Software EngineerCompare bagging, boosting, random forests, and bias-variance
Machine Learning
1
0
January 15, 2026
Meta
Medium
Data ScientistWhat features and feature selection would you use?
Machine Learning
1
0
August 10, 2025
Point72
Medium
Machine Learning EngineerDesign a News-Filtering Prompt
Machine Learning
1
0
January 21, 2026
Snapchat
Medium
Machine Learning EngineerDiscuss ML Project Tradeoffs
Machine Learning
1
0
April 28, 2026
Natoora
Medium
Data AnalystWhen use LLMs for reporting?
Machine Learning
1
0
January 18, 2026
Stripe
Hard
Data ScientistDesign a leak-free time-split model
Machine Learning
1
0
October 13, 2025
Meta
Hard
Data ScientistOptimize IG Shopping ranking with multiple objectives
Machine Learning
1
0
October 13, 2025
Cadence
Medium
Machine Learning EngineerExplain Transformer Positional Encoding
Machine Learning
1
0
January 5, 2026
Bytedance
Medium
Data ScientistAnalyze Product Launch and Creator Engagement
Machine Learning
0
0
March 14, 2026
Xometry
Medium
Machine Learning EngineerExplain an End-to-End ML Project
Machine Learning
0
0
March 7, 2026
Inception
Medium
Machine Learning EngineerImplement Autoregressive Decoding in PyTorch
Machine Learning
1
0
January 10, 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.