{
  "id": 8963122,
  "title": "Designing a Better Baby Name Search: What I Learned About Search UX, Unicode, and Relevance",
  "url": "https://urgent.news/2026/09/21/designing-a-better-baby-name-search-what-i-learned-about-search-ux",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-21T16:57:46.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/attaullah_siddiqi/designing-a-better-baby-name-search-what-i-learned-about-search-ux-unicode-and-relevance-3lmf"
  },
  "original_language": "en",
  "account": "Creating a more intuitive baby name search experience is a complex task that involves more than just storing and retrieving data. When I began working on the baby name section of Nurturepedia, I initially saw it as a simple CRUD problem. However, I soon realized that people don't always search for names using exact spellings. They look for names based on meaning, try various spellings, consider cultural preferences, and sometimes even know the desired sound without knowing the spelling. This transformed the problem into a more intricate engineering challenge.\n\nThe first crucial step was to rethink the data model. Storing a name as a single string was insufficient. To improve the search experience, it's essential to treat a baby name as a record containing multiple fields of context. For instance:\n\n{ name: Amélie, normalizedName: amelie, gender: girl, meanings: [work, industrious], origins: [French], religions: [Christianity, Neutral], alternateSpellings: [Amelie], countries: [France, Canada, United States] }\n\nHere, `name` represents what the user sees, while `normalizedName` is what the application uses for searching. This separation is particularly useful when dealing with names that contain accents, diacritics, alternate spellings, or characters from different writing systems. Unicode can sometimes disrupt a search experience by creating different representations for what appears to be the same character. JavaScript developers often encounter this issue. For example, the character é can be represented as a single code point or as a base character combined with a combining accent. JavaScript's `String.prototype.normalize()` function helps address these variations.\n\n```javascript\nconst value = \"Amélie\";\nconst normalized = value.normalize(\"NFKD\").replace(/\\p{Diacritic}/gu, \"\").toLowerCase();\nconsole.log(normalized); // amelie\n```\n\nHowever, remember that the normalized value should never replace the original display value. They serve different purposes. The display value should remain as Amélie for users, while the normalizedName `amelie` should be used for matching.\n\nExact matching alone isn't enough. When users search for a name, they don't always know the correct spelling. For example, if someone searches for \"amel,\" a basic substring query might not yield the best results. The search experience becomes a relevance problem rather than a simple database lookup. A practical ranking model could prioritize results in this order: exact name match, exact normalized match, prefix match, alternate spelling match, meaning match, and broader relevance. This approach helps the search engine understand what the user probably meant rather than just matching what's in the database.\n\nMongoDB Search offers powerful tools for relevance-oriented search, including autocomplete, compound queries, filtering, faceting, and scoring. For larger datasets, these features provide more control than relying on repeated regex queries. Autocomplete is more than just a nice UI feature; it's an integral part of the search architecture. When a user starts typing, the application can suggest names like Zayn, Zaynab, Zayla, and Zayyan, making the search process more interactive and helpful. However, autocomplete should be designed to provide highly relevant suggestions without overwhelming the user with irrelevant options. Filters can significantly improve the search experience but should be designed thoughtfully. Baby-name websites often accumulate numerous filters, such as gender, origin, religion, meaning, country, style, popularity, and even astrological signs. While adding filters is relatively straightforward, designing an intuitive interaction around those filters is more challenging. Users might confuse terms like origin and culture, or they may search for names based on cultural associations rather than linguistic origins. To address this, it's crucial to maintain a structured metadata system that preserves distinctions between related concepts. For instance:\n\n```json\n{\nname: \"Example\",\norigins: [\"Arabic\"],\nlanguages: [\"Arabic\", \"Urdu\"],\ncountries: [\"Pakistan\", \"United Kingdom\"],\nmeanings: [...],\nalternateSpellings: [...]\n}\n```\n\nBy storing information independently, the database can provide more precise search results, better filtering, more informative URLs, and clearer explanations of why a result matched. Multicultural data requires extra care because a name can have different pronunciations, spellings, meanings, or cultural associations across various countries and languages. Storing names with multiple origins, languages, countries, and meanings allows for a more nuanced search experience that respects the complexity of multicultural data.",
  "summary": "A baby-name database sounds like a straightforward CRUD problem. Store a name. Store its meaning. Add a few filters. Put a search box on top. That was roughly how I looked at it when I started working on the naming side of Nurturepedia . Then I started looking at how people actually search for names. They don't always type the exact spelling. They search by meaning. They try different spellings.…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}