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    Home»Machine Learning»The Making of Search — From Street Food to Michelin Stars | by Fergie Liang | The Recall Memo | Jun, 2025
    Machine Learning

    The Making of Search — From Street Food to Michelin Stars | by Fergie Liang | The Recall Memo | Jun, 2025

    Team_AIBS NewsBy Team_AIBS NewsJune 10, 2025No Comments1 Min Read
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    How fashionable search methods work, defined by way of cooks, eating places, and cuisines

    As a search product supervisor, I’ve discovered one of the best ways to grasp how search works — and the way search methods differ — is thru a easy cooking analogy.

    If a search result’s the dish, then the search algorithm is the chef, and looking out is the method of cooking it.

    Think about every search system as a kitchen. Each time you hit ‘search,’ that kitchen fires up behind the scenes to prepare dinner up the search outcomes you ordered. Each app you employ is a restaurant with its personal kitchen.

    Picture by Sebastian Coman Photography on Unsplash

    Within the last memo, I described looking out as a matchmaking course of made up of three levels: intent → match → rank. Behind the scenes, this breaks down right into a technical pipeline of 4 important steps: indexing, question processing, retrieval, and rating.

    Consider these 4 steps as making a dish:

    • Indexing = farming the uncooked components
    • Question understanding = washing and chopping
    • Retrieving = cooking
    • Rating = plating the dish.

    Let me clarify what I imply and why it is smart.



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