hardGlean Perplexity Harvey

Your RAG system aces your eval set but fails on real user queries. How do you close the gap?

A 90% eval score and angry users at the same time means your eval set doesn't look like reality. The fix is to make evaluation track production, not the other way around. Here is how.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

A 90% eval score and angry users at the same time means your eval set doesn't look like reality. The fix is to make evaluation track production, not the other way around. Here is how.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·What this job actually isFree15mFive habits from normal software that will hurt you hereThe hardest part of moving into applied AI is not the new material, it is the instincts you already have. Five of them are actively wrong here. This lesson names each one, what replaces it, and why the substitution is not optional.Applied AI Engineering·Prompting as an engineering disciplineFree13mThe prompt failures you will meet, and what each one meansSix failures account for most of what goes wrong with a prompt in production. Each has a distinct cause and a distinct fix, and recognising which one you are looking at is faster than any amount of rewording.

UP NEXT ON YOUR JOURNEY

Next in this trackShould you build agent orchestration yourself or use a framework like LangGraph? How do you decide?Next in this trackWorkflows versus agents: how much autonomy should you actually give an AI system, and how do you decide?Next in this trackBuild a customer-support agent over a fake product/SQL database: tool-calling, retrieval, and a control loop.

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