OpenAI
Medium
Software EngineerDebug a Machine Learning Pipeline
Machine Learning
41
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.
522Questions
115Companies
Easy 52 • Medium 258 • 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
OpenAI
Medium
Machine Learning EngineerDebug Transformer and Add KV Cache
Machine Learning
59
0
February 1, 2026
OpenAI
Hard
Machine Learning EngineerBuild and troubleshoot image classification and backprop
Machine Learning
63
0
July 27, 2025
OpenAI
Medium
Machine Learning EngineerDebug MiniGPT and Backpropagate Matmul
Machine Learning
46
0
April 3, 2026
Cresta
Medium
Machine Learning EngineerImplement greedy and beam decoding
Machine Learning
51
0
September 6, 2025
TikTok
Medium
Data ScientistCompare Random Forests and Boosted Trees: Bias, Variance, Speed
Machine Learning
155
4
July 12, 2025
Waymo
Easy
Data ScientistCompare two rare-event detection models statistically
Machine Learning
47
0
January 17, 2026
OpenAI
Hard
Software EngineerDebug a failing ML classifier
Machine Learning
50
0
July 28, 2025
OpenAI
Hard
Machine Learning EngineerCompute Matrix Prefix Products And Gradients
Machine Learning
37
0
April 2, 2026
Anthropic
Hard
Software EngineerImplement and analyze custom attention
Machine Learning
37
0
September 6, 2025
Adyen
Easy
Machine Learning EngineerBuild a Payment Fraud Detection Model
Machine Learning
21
0
May 9, 2026
OpenAI
Hard
Machine Learning EngineerImprove Training With Noisy Annotators
Machine Learning
40
0
April 2, 2026
OpenAI
Hard
Machine Learning EngineerDerive Backpropagation for Matrix-Product Layers
Machine Learning
30
0
February 5, 2026
Anthropic
Medium
Machine Learning EngineerExplain batch inference design
Machine Learning
43
0
February 27, 2026
Voleon Group
Hard
Data ScientistDesign and diagnose a regression pipeline
Machine Learning
40
0
October 13, 2025
Microsoft
Easy
Data ScientistExplain KNN and how to tune it
Machine Learning
32
0
January 17, 2026
Waymo
Easy
Data ScientistHow predict vehicles’ turn direction at intersection?
Machine Learning
31
0
January 17, 2026
Scale AI
Easy
Machine Learning EngineerExplain LLM post-training methods and tradeoffs
Machine Learning
28
0
February 12, 2026
LinkedIn
Easy
Data ScientistDesign a short-video recommender system
Machine Learning
24
0
February 1, 2026
Jane Street
Hard
Machine Learning EngineerBuild a time-series forecasting model
Machine Learning
21
0
August 1, 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.