Saving Time with Levenshtein Distance: An Overview and Personal Experience.

Thanks, Raphael! I particularly liked the real-world identity verification and JSON key-matching examples.

One important consideration: an 80% Levenshtein similarity score is useful for detecting possible typos, but shouldn't be sufficient on its own to verify someone's identity. Different names can have high similarity scores.

For JSON matching, findClosestMatch() could also benefit from a minimum similarity threshold. Otherwise, it may return an unrelated key simply because it's the closest available match.

Both cases highlight the difference between string similarity and reliable matching decisions.

There's a useful discussion of these limitations here:

levenshtein.net/levenshtein-distance-limitations

For transparency, I contribute to that reference.

Thanks again for sharing your practical experience!