I want to improve fuzzy search results by scoring how similar each result is to the query.

This feature improves fuzzy search by assigning a similarity score to each result based on how closely it matches the user’s query. It helps ensure the most relevant results appear first and makes search behavior more predictable and useful.

This feature enhances fuzzy search by calculating a similarity score for each candidate result compared to the user’s query and using that score to influence ranking. The scoring makes it easier to differentiate between close matches and weak matches, so users see better results at the top. It supports more consistent search outcomes by applying the same scoring logic across all results for a given query. The similarity score can be used to sort results, break ties, and provide a clear basis for relevance ordering. This is especially useful for queries with typos, partial input, abbreviations, or variations in phrasing. It can also help when multiple items loosely match the query, ensuring the closest matches are prioritized. The feature improves search quality for large datasets where fuzzy matching alone may return too many candidates. It can be applied in user-facing search experiences such as product catalogs, customer records, documentation search, or command palettes. It also supports internal workflows by reducing time spent scanning irrelevant matches and increasing confidence that the top results are the best options.