ai-cleaning-data
Use DSPy to normalize and fix messy data fields at scale. The core pattern - messy field value + field type/context → cleaned value + confidence - lets you handle inconsistent addresses, company names, dates, phone numbers, and free-text fields without writing a rule for every edge case.
The most effective approach: sample anomalies first, infer normalization rules, then apply deterministically where possible and use the LM only for ambiguous cases.
Step 1 - Understand t
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