AI Engineer Interview Q&A: Graph RAG, Agent Memory, Observability, Guardrails

Bashiri Smith · Facebook reel · 2026-08-25 · 1:32 · 4,434 views · Open on Facebook

Topics: Retrieval-Augmented Generation (RAG), AI Agents, Tool Use & MCP, LLMOps, Deployment & Monitoring · Level: intermediate

Summary

A mock AI engineering interview with four questions and short model answers. It covers when graph RAG is a better choice than plain vector RAG, how to design agent memory and when to bring in a human, what an LLM observability and tracing stack should capture, and how to add guardrails while managing what they cost. It ends by promoting the creator's Skool community, BASWE.Ai Engineer.

Key points

• Graph RAG beats vector RAG when the answer depends on relationships rather than semantic similarity. Example: multi-hop questions about how people, companies or events connect.
• Vector RAG is good at finding relevant chunks. Only use a graph when relationships clearly improve retrieval, because building and maintaining the graph costs a lot.
• Agent memory: keep short-term working state (the current task) separate from long-term memory, which should store only durable information that helps future tasks.
• Bring in a human when confidence is low, when the action is irreversible or high-risk, or when the agent is about to cross an important permission boundary.
• Observability: trace the whole request, including prompt, retrieval, tool calls, model responses, latency, token usage, cost and errors.
• Link traces to evaluation so you can tell whether a failure came from retrieval, the model, a tool or orchestration. The goal is to reproduce and diagnose failures, not just log them.
• Guardrails go wherever model output creates real risk: schema validation, permission checks, content filters, or a separate verifier model.
• Guardrails cost extra latency and money and can cause false positives, so make them stronger as the risk goes up.

Resources mentioned

• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more

Try this

• Practice short, structured answers to these four interview questions: graph vs. vector RAG, agent memory and human-in-the-loop, LLM observability, and guardrails.
• Comment 'join' or use the caption link to join the BASWE.Ai Engineer community (optional, creator promotion).