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Design a document summarization pipeline that handles long documents at high throughput.

Summarizing a 200-page contract is not one LLM call: it is chunking, hierarchical reduction, and a faithfulness check so you never invent facts. Learn the map-reduce pattern, when long-context beats it, and how to evaluate summaries at scale.

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

Summarizing a 200-page contract is not one LLM call: it is chunking, hierarchical reduction, and a faithfulness check so you never invent facts. Learn the map-reduce pattern, when long-context beats it, and how to evaluate summaries at scale.

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LEARN THE BACKGROUND

No lesson covers this question directly yet. These teach the surrounding topic from the beginning.

Applied AI Engineering·The interviewPremium14mDriving the design conversationA design round is a conversation you are expected to lead, not a question you answer. This lesson is the shape that works, the four moments that decide the outcome, and the two classic ways strong candidates lose one.Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.
UP NEXT ON YOUR JOURNEY
Next in this trackDesign an LLM-based content moderation system that screens user content at platform scale.Next in this trackDesign a personalization service that tailors LLM responses to each user's context and history.Next in this trackDesign an AI writing assistant (Grammarly/Notion-style) for rewriting, grammar, and tone.
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