Data Science | ML | AI Resources

Asked in a Netflix AI Engineer interview 👇 A Netflix user watches a movie and says, “Show me something like this, but more exciting.” How would you design a GenAI recommendation system that understands the user’s intent and generates personalized recommendations? "More exciting" is a direction, not a query. The request is an anchor plus a vector, and a plain similarity search answers the wrong question. 1. Parse the instruction into anchor, axis and constraints - Anchor: the title they just watched - Axis and magnitude: pace and intensity, moved up a notch - What must not change: genre, language, tone, maturity rating 2. Label the catalogue on the axes people actually use - Pace, intensity, humour, complexity, ending type, violence - Derive them from metadata, subtitles and trailers; LLM-tag what is missing - Human-verify the axes that drive the most requests — this is the real work 3. Move the vector, then retrieve - Take the anchor embedding, add a delta along the excitement axis, run ANN over the catalogue - Then apply availability, language and maturity filters 4. Personalise the result, do not replace it - Blend with the member's taste model so two people asking the same thing get different rows - The instruction constrains, the taste model orders 5. Generate the explanation, never the item - The LLM writes "faster paced, same heist premise"; the catalogue decides what exists - Ungrounded generation recommends titles you do not have the rights to 6. Keep the session open - "Even more" should travel further along the same axis, not reset - Log every direction request — they are free, precise preference labels

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