Product design question
How would you make sure Amazon gives relevant results if a user types in the product details in local language?
- Amazon
- Product design
- Medium
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What this question tests
Product design for multilingual search relevance: understanding NLP challenges and measurable ways to close a language gap.
How to approach it
- Clarify scope: which markets and languages, since India alone has many regional languages with different scripts and transliteration patterns.
- Identify the user: shoppers who search in their native language or a mix of English and local language (code-mixing), common in markets like India.
- Name the pain point: search relevance models trained mostly on English queries fail to match local language terms to the right product attributes.
- Propose a solution: a multilingual query understanding layer that maps local language and transliterated terms to canonical product attributes, plus expanding product catalog metadata with local language synonyms.
- Prioritize the highest volume regional languages first based on search query data, rather than trying to cover all languages equally at once.
- Define success as local language search zero-result rate and click-through rate on local language queries compared with English queries.
What a strong answer includes
- Names the real technical challenge, code-mixed and transliterated queries, which is specific to markets like India rather than a generic translation problem.
- Proposes prioritizing languages by query volume, a practical resourcing decision many candidates skip.
- Uses zero-result rate as a concrete, measurable proxy for search relevance failure in local languages.
Common mistakes
- Treating this as a simple machine translation problem instead of addressing code-mixing and transliteration.
- Proposing to support all languages equally without prioritizing by actual query volume.
Likely follow-up questions
- How would you evaluate search relevance quality for a language you do not speak?
- How would you handle catalog metadata for a long tail of low volume languages?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop