Phase 10: Career, Portfolio & Interview Engineering · 45 min · Claude Code · Python

Interview Engineering: System Design for AI-Native Products

'I'd use an LLM to call a refund API' is an architecture guess. 'Here's the spec that defines a correct refund, the harness that constrains the agent, the quality gate that catches regressions, and how I'd know in production if it's wrong' is a system design answer.

What you will learn

Explain why 2026 AI/ML system design interviews score operational depth (evaluation, cost, monitoring) explicitly rather than as a bonus
Use the spec→harness→orchestration→quality→observability framework as a repeatable structure for a system design interview answer
Proactively raise tradeoffs (cost, latency, failure modes, drift monitoring) instead of waiting for the interviewer to ask
Practice applying the framework to a concrete 'design an AI-native feature for X' prompt

Introduction

Interview Engineering: System Design for AI-Native Products

Objective

Check whether a system design interview answer outline addresses all five framework stages (spec, harness, orchestration, quality gates, observability) and proactively raises a failure mode or tradeoff.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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