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Medium
Machine Learning EngineerExplain bias–variance, overfitting, and vanishing gradients
Machine Learning
9
0
January 22, 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
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Uber
Hard
Data ScientistDesign ETA prediction for Uber rides
Machine Learning
12
0
October 13, 2025
LinkedIn
Medium
Machine Learning EngineerExplain activations, losses, and Adam
Machine Learning
13
0
February 11, 2026
Reddit
Medium
Machine Learning EngineerModel y from x and interpret distributions
Machine Learning
9
0
February 12, 2026
Reddit
Medium
Machine Learning EngineerBuild and evaluate click prediction models
Machine Learning
10
0
September 6, 2025
Upstart
Hard
Data ScientistAddress Missing Income Bracket in California Housing Data
Machine Learning
30
0
August 4, 2025
OpenAI
Medium
Machine Learning EngineerDefend a Research Direction and Experiment Design
Machine Learning
14
0
April 13, 2026
Bank of America
Medium
Data ScientistExplain core probability and ML statistics concepts
Machine Learning
13
0
February 11, 2026
Amazon
Hard
Data ScientistDesign an end-to-end spam detection system
Machine Learning
12
0
October 13, 2025
Dandy
Medium
Machine Learning EngineerWhy do transformers struggle with long context?
Machine Learning
6
0
April 5, 2026
Tesla
Medium
Software EngineerHow to Identify Best Battery Group
Machine Learning
13
0
October 26, 2025
OneMain Financial
Medium
Data ScientistHandle Missing Values and Outliers in Machine Learning
Machine Learning
46
0
July 12, 2025
Citibank
Medium
Data ScientistExplain PD model validation steps
Machine Learning
5
0
July 26, 2025
Google
Hard
Data ScientistModel Soccer Shot Conversion
Machine Learning
8
0
December 29, 2025
EliseAI
Medium
Software EngineerExplain prompt engineering strategies for chatbots
Machine Learning
13
0
September 6, 2025
LinkedIn
Medium
Machine Learning EngineerAnswer practical ML foundations questions
Machine Learning
13
0
February 18, 2026
Applied Intuition
Medium
Machine Learning EngineerImplement and explain positional encoding
Machine Learning
10
0
September 6, 2025
Amazon
Medium
Data ScientistCompare RNNs and Transformers for Long-Sequence Text Classification
Machine Learning
45
0
August 4, 2025
Upstart
Medium
Data ScientistLeverage Existing Model for Low Credit Score Applicants
Machine Learning
24
0
August 4, 2025
Voleon Group
Medium
Data ScientistDescribe Your Machine Learning Project Experience
Machine Learning
13
0
August 4, 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.