Fuzzy matching is a technique used to identify non-exact matches of a target item, crucial in search engine frameworks to deliver relevant results despite typographical errors or variations in a query. Predominantly, search engines use the Levenshtein Distance, which measures the number of edits needed to transform one string into another. While effective, it can introduce false positives, prompting some frameworks to also utilize the Damerau-Levenshtein distance, which allows character transpositions. This article highlights the challenges of fuzzy matching, particularly its impact on result relevance, and provides strategies to enhance search accuracy, such as combining queries with varying fuzziness levels. Examples from a movie catalog demonstrate how different configurations affect search outcomes, illustrating that while fuzzy matching improves user experience by accommodating errors, careful calibration is essential to minimize irrelevant results. The discussion also emphasizes that effective search relies on a balance of techniques, including fuzzy matching, to ensure user-friendly and relevant search experiences.