Interview Prep & System Design

A dedicated track for the two things the rest of the handbook doesn't directly prepare you for: getting asked about this material under interview pressure, and designing a full AI system on a whiteboard in 45 minutes.

This track assumes you've worked through (or are working through) the core courses — it drills the same material from an interviewer's angle, not a new curriculum.

flowchart LR
  subgraph QA["Question banks"]
    F[LLM & Transformer Fundamentals]
    R[RAG]
    A[Agents]
    E[Evals & Production]
  end
  subgraph SD["System design case studies"]
    D1[RAG System]
    D2[Agent Platform]
    D3[LLM Serving]
    D4[Eval Pipeline]
  end
  QA --> SD

Question banks

Each page groups 6-10 questions by topic, tagged by level (L1 screening, L2 mid-level, L3 senior/staff). Every answer explains the reasoning and the likely follow-up — not just the "correct" one-liner.

Page	Covers
LLM & Transformer Fundamentals	Attention, tokenization, training dynamics, sampling
RAG	Chunking, retrieval, reranking, hallucination
Agents	Tool use, memory, orchestration, failure handling
Evals & Production	Eval design, cost, latency, safety, incident response

System design case studies

Each case study walks the full interview process: clarifying questions → requirements → architecture → deep dive → tradeoffs → failure modes → likely follow-ups. Read these after the question banks — they assume the vocabulary the Q&A pages build.

Page	System
Design a RAG System	Retrieval-augmented Q&A over a large private corpus
Design an Agent Platform	Multi-tenant platform running autonomous coding/support agents
Design a Coding Agent & IDE Assistant	Autonomous coding assistant (Claude Code / Cursor style) with repo mapping and sandboxed edits
Design an Agent Data Flywheel	Self-improving agent telemetry, execution verification, and SFT/DPO pipeline
Design an Enterprise Hybrid Vector Search Engine	Sub-50ms HNSW + BM25 hybrid vector database for 1B vectors
Design an AI Safety & Guardrails Gateway	Sub-20ms streaming reverse proxy for PII scrubbing and prompt injection defense
Design an LLM Serving System	Low-latency, high-throughput model inference at scale
Design an Eval Pipeline	Continuous quality measurement for an LLM product in production

How to use this track

1. Skim the question bank for a topic; for each question, cover the answer and try to say it out loud before reading.
2. Note which follow-ups you couldn't answer — that's your gap, go back to the linked lesson.
3. Do one system design case study per sitting, on a real whiteboard or paper, before reading the page's walkthrough. Compare your clarifying questions to the page's list first — most candidates lose points here before the architecture even starts.

---

🤝 1:1 Mentorship & Enterprise AI Consulting

Need personalized career guidance, mock interviews, or AI architecture consultation from the handbook author?

• 🎯 1:1 Guidance & Mock Interviews: Book a 1:1 session with Nikhil Pentapalli on Topmate.
• 🏢 Enterprise AI Consulting: For enterprise AI systems design, RAG, agent architecture, and team training, reach out directly to psss.nikhil@gmail.com.

---

Next

Start with LLM & Transformer Fundamentals, or jump straight to Design a RAG System if you're prepping for a system design round specifically.