Architect a system where a user queries an LLM, the system finds the relevant documents, and then returns the response.

"I interviewed for an Agentic AI Developer role at Cisco, grade 8, and it was only two rounds. The first round was Python-heavy: two basic DSA problems, one around binary trees and one simple array question, plus a role-specific problem where I had to return the top three relevant documents from a set of 10 to 20 prompts using my own RAG-style chunking approach. The second round was much more domain-specific and centered on architecting a system where a user queries an LLM, the system finds relevant docs, and then returns a response. They also went into storage internals and my past work, so it definitely made me think a lot. What felt different was how focused the whole thing was on agentic AI instead of just a standard software interview."