AI interviews require knowledge of RAG and agents

AI system design interviews now include practical questions about RAG, agents and LLM gateways. The questions address architecture, chunking and security in production AI systems.

AI system design interviews are becoming more technical and focused on real production challenges. Senior engineers are now assessed on how they design RAG systems, LLM gateways and AI agents. The material includes 25 practical questions about RAG, focusing on chunking, embeddings and security. There are also 30 questions about AI agents in production, addressing how to make them 'production-ready'. One of the central themes is how to prevent users from retrieving documents they are not allowed to access, with an emphasis on authorization during retrieval. The questions also cover how to reduce hallucinations by improving retrieval quality and restricting answers to trusted sources.

There is also a focus on gateway design, with authentication, rate limits and cost tracking. The content is based on materials published by sources such as AI System Design Interview Questions and Answers for Senior Engineers and What Is an AI Agent? A Complete Interview Guide to Agentic AI Development. Previously, the focus was mainly on language models, but it now covers architecture, security and scalability. Practical questions about RAG, agents and LLM gateways are becoming common, with an emphasis on real-world scenarios and production implementation. The shift reflects greater complexity in interviewers' expectations, as they look for engineers capable of designing robust and scalable solutions.

The questions range from choosing the chunk size to reducing hallucinations and designing gateways with authentication and cost tracking. This evolution may frustrate candidates who lack experience with distributed systems and security in production environments.

How to avoid data leakage in AI systems

AI system design interviews now require knowledge of architecture, security and scalability challenges. Choosing the chunk size for RAG requires balancing loss of context against the inclusion of irrelevant information. Authorization during document retrieval prevents leakage of sensitive data. The questions include scenarios based on real problems, such as scaling systems, with a focus on interviews for senior developers.