OpenAI
Hard
Machine Learning EngineerImprove classifier with noisy multi-annotator labels
Machine Learning
832
3
February 11, 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.
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
Hard
Data ScientistDebug and fix a PyTorch Transformer training loop
Machine Learning
250
0
October 13, 2025
OpenAI
Hard
Machine Learning EngineerDebug transformer and train classifier
Machine Learning
144
0
August 4, 2025
OpenAI
Medium
Machine Learning EngineerImplement 1NN with NumPy
Machine Learning
161
2
May 19, 2026
Amazon
Hard
Machine Learning EngineerExplain Core ML Interview Concepts
Machine Learning
178
0
April 27, 2026
OpenAI
Medium
Software EngineerImplement and Debug Backprop in NumPy
Machine Learning
72
1
September 6, 2025
LinkedIn
Medium
Data ScientistExplain Logistic Regression, Backprop, and Adam
Machine Learning
139
0
April 5, 2026
OpenAI
Hard
Machine Learning EngineerDebug a broken Transformer implementation
Machine Learning
146
0
January 21, 2026
OpenAI
Medium
Machine Learning EngineerFilter Bad Human Annotations
Machine Learning
99
0
April 3, 2026
Anthropic
Medium
Software EngineerDebug a GRPO training loop and explain ratios
Machine Learning
94
0
February 19, 2026
Anthropic
Medium
Machine Learning EngineerDesign a Double Descent Experiment
Machine Learning
59
0
April 19, 2026
OpenAI
Hard
Machine Learning EngineerDiagnose Transformer training and inference bugs
Machine Learning
83
0
August 11, 2025
Capital One
Easy
Data ScientistDesign robber detection from surveillance video
Machine Learning
62
0
February 22, 2026
OpenAI
Hard
Machine Learning EngineerTrain a classifier and analyze dataset
Machine Learning
56
0
September 6, 2025
OpenAI
Hard
Machine Learning EngineerDebug a transformer training pipeline
Machine Learning
40
0
July 31, 2025
Cresta
Medium
Software EngineerDesign sequence decoding with greedy and beam search
Machine Learning
74
0
August 8, 2025
Luma AI
Hard
Machine Learning EngineerImplement and visualize in-place augmentations
Machine Learning
36
0
July 17, 2025
OpenAI
Medium
Machine Learning EngineerDebug a Broken Transformer
Machine Learning
72
0
March 3, 2026
Anthropic
Medium
Software EngineerImplement and derive backprop from scratch
Machine Learning
37
0
September 6, 2025
Uber
Medium
Data ScientistEvaluate Promotions for Uber Eats Users
Machine Learning
67
0
April 30, 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.