Interview
Questions

Tell me about a time you had to handle hallucinations in an LLM application. What strategies did you use to detect and mitigate them?

Describe a situation where you had to optimize for latency versus quality in an AI system. How did you make the tradeoff?

Tell me about a RAG system you built or improved. What challenges did you face with retrieval quality and how did you address them?

How have you approached prompt engineering for a production system? What iteration process did you follow to improve results?

Describe a time you had to manage token costs while maintaining output quality. What was your approach?

Tell me about an AI agent or multi-step LLM system you designed. What were the key architectural decisions and why?

How have you evaluated LLM outputs for quality in production? What metrics or approaches did you use?

Describe a situation where you had to decide between fine-tuning versus prompt engineering. What factors influenced your decision?

Tell me about a time an AI system behaved unexpectedly in production. How did you debug and resolve it?

How have you approached safety and content moderation in AI applications? Give a specific example.

Describe your approach to testing and validating AI features before shipping. How do you handle non-deterministic outputs?

Tell me about a time you had to explain AI limitations to stakeholders. How did you set realistic expectations?

Describe how you approach model selection for a new AI feature. What criteria do you use to choose between different LLMs or approaches?

Tell me about a time you shipped an AI feature when you were not 100% confident in its behavior. How did you mitigate risk?