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    From manual summaries to scalable, on-brand AI writing

    Client

    Flashbook

    Industry

    EdTech / Learning Platform

    The questions that shaped the project

    We want to scale summaries without losing our tone of voice

    We want every summary to fully reflect the book's core ideas

    We want consistency across hundreds of summaries

    We want summaries that feel editorial — not automated

    We want a system that works for short and very long books

    We want quality control without manual rewriting

    The Challenge

    Flashbook creates concise, high-quality summaries of self-help books, designed to be read or listened to in just 15–20 minutes. As the library grew, manual summarization stopped scaling. One-shot AI summaries often compressed too aggressively — skipping arguments, losing nuance, or drifting from Flashbook's tone. The real challenge wasn't whether AI could generate summaries. It was how to use AI without losing structure, editorial quality, or the feeling that a human had carefully written the result.

    The Goal

    Design a summarization system that could scale with Flashbook's content library while preserving the quality users expect. Summaries needed to remain complete, structured, and unmistakably in Flashbook's voice — regardless of a book's length or complexity. In short: build an AI-driven workflow that supports Flashbook's editorial process, rather than replacing it.

    My Approach

    1 / 4

    Step 1

    Structured Content Preparation

    Extract, clean, and split into logical chunks.

    Books are first extracted, cleaned, and split into logical chunks.


    This ensures:

    • Each part of the book is handled deliberately
    • No chapter is ignored or over-compressed
    • The system works reliably beyond model context limits

    Step 2

    Chunk-Level Summaries

    Capture depth in Flashbook's voice.

    Each chunk is summarized individually using prompts tailored to:


    • Capture key arguments and examples
    • Maintain Flashbook's tone and pacing
    • Avoid generic or shallow summaries

    This guarantees depth and consistency, even across long or complex books.

    Step 3

    Hierarchical Aggregation

    Merge content logically across chapters.

    Chunk summaries are then combined in stages using aggregator prompts that:


    • Merge content logically
    • Remove overlap and repetition
    • Preserve narrative flow across chapters

    For very large books, aggregation runs multiple times until a single coherent summary remains.

    Step 4

    Final Editorial Refinement

    One voice, one polished result.

    A final refinement pass ensures:


    • One consistent voice throughout
    • Clear structure and readability
    • No traces of "stitched" output

    The result feels like a carefully written summary, not an automated one.

    Results

    Summarization from input to final output
    Consistent Flashbook tone across the entire library
    Reliable handling of long and complex books
    Editorial-quality summaries at scale
    Faster production without sacrificing depth
    "Laurens is quick to understand, takes ownership immediately, and doesn't stop until it's truly done. This mindset helped us automate our summaries from A to Z using AI. If you have an AI or automation challenge, I can absolutely recommend Laurens."
    Thomas

    Thomas

    Co-founder, Flashbook

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