LLM & Generative AI Interview Questions

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.

690Questions
141Companies
Easy 70 • Medium 375 • Hard 245Difficulty 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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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.