UiPath
Easy
Machine Learning EngineerExplain Core ML Fundamentals
Machine Learning
0
0
January 10, 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.
500Questions
107Companies
Easy 1 • Medium 9 • Hard 196Difficulty 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
Bnp
Easy
Data ScientistWhat are linear regression assumptions?
Machine Learning
1
0
January 5, 2026
Amazon
Medium
Machine Learning EngineerExplain modern modeling and alignment methods
Machine Learning
1
0
December 20, 2025
Tradedesk
Hard
Data ScientistDesign a CVR model for RTB bidding
Machine Learning
1
0
December 10, 2025
Meta
Medium
Data ScientistBuild a model to infer home vs office vs public
Machine Learning
0
0
October 13, 2025
Boston Consulting Group
Medium
Data ScientistExplain AUC, activations, ensembles, and imbalance
Machine Learning
1
0
October 13, 2025
Meta
Hard
Data ScientistDesign hashtag recommender with cold start
Machine Learning
1
0
October 13, 2025
C3 AI
Easy
Data ScientistIdentify a classic bagging algorithm
Machine Learning
0
0
August 9, 2025
Meta
Medium
Data ScientistEvaluate New Model's Performance Against Existing System
Machine Learning
1
0
August 4, 2025
J.P. Morgan
Medium
Data ScientistExplain Overfitting and Transformer Basics
Machine Learning
1
0
April 7, 2026
Ancestry
Medium
Software EngineerExplain learning paradigms, loss, and embeddings
Machine Learning
1
0
February 11, 2026
Tubitv
Medium
Machine Learning EngineerExplain ML basics and recommender tuning
Machine Learning
0
0
January 28, 2026
Bytedance
Hard
Data ScientistHow to deploy and tune multimodal models?
Machine Learning
1
0
January 10, 2026
Morgan Stanley
Medium
Data ScientistExplain Chunking for Financial RAG
Machine Learning
0
0
March 1, 2026
Bytedance
Easy
Data ScientistExplain Train-Test Performance Gap
Machine Learning
0
0
February 14, 2026
Reddit
Medium
Machine Learning EngineerAnalyze CTR Data and Train Model
Machine Learning
0
0
February 1, 2026
Natoora
Medium
Data AnalystWhen use LLMs for reporting?
Machine Learning
1
0
January 18, 2026
Qube
Hard
Software EngineerCompare bagging, boosting, random forests, and bias-variance
Machine Learning
1
0
January 15, 2026
Bytedance
Medium
Machine Learning EngineerCompute Sentence Similarity
Machine Learning
0
0
January 7, 2026
Uber
Medium
Data ScientistDesign a ride-hailing ETA system
Machine Learning
0
0
January 3, 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.