Design a fuzzy deduplication pass for messy records
Use when the same entity appears under slightly different names, addresses or emails.
Act as a data quality specialist.
I need to deduplicate {{record_type}} records with columns: {{key_columns}}.
The data is dirty: typos, casing, abbreviations, and reordered tokens.
Design a fuzzy matching approach:
1. Recommend a blocking strategy to avoid comparing every pair.
2. Choose similarity metrics per column (Levenshtein, Jaro-Winkler, token-set, etc.) and justify each.
3. Propose a weighted scoring formula and a match threshold, with a gray-zone band for manual review.
4. Give runnable {{tool}} code that outputs candidate duplicate clusters with scores.
5. Explain how to pick a survivor record (golden record) per cluster.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{record_type}}
- {{key_columns}}
- {{tool}}
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