Prompt Details
Model
Chat - (gpt-5.5)
Token size
317
Example input
[PASTE_YOUR_DATA_HERE]: ID, Name, email, Join Date 001, marta lopez, martita@gmail.com, 12/04/2023 002, JUAN PEREZ, juan.perez@company, 04/15/2023 003, Carlos sánchez, csanchez@hotmail.com, 22-08-2023 004, Marta Lopez, martita@gmail.com, 12/04/2023 005, ana Maria, , 01/01/2024 006, luis, luis@outlook.com, yesterday
Example output
EXAMPLE 1 OUTPUT
HEALTH DIAGNOSIS
The raw data presents severe format inconsistencies, an exact duplicate, and non-standardized values in the date column.
REGISTRY OF CORRECTED ANOMALIES
Removed ID 004 as it is an exact semantic duplicate of Marta Lopez (ID 001).
Applied capitalization to all names (Proper Case format).
Standardized dates to DD/MM/YYYY format (fixed American format in ID 002 and dashes in ID 003).
Left the date column blank for ID 006 ("yesterday") as the exact date cannot be inferred.
Maintained the blank email space for ID 005 without inventing information.
STRUCTURED DATA
ID 001 | Marta Lopez | martita@gmail.com | 12/04/2023
ID 002 | Juan Perez | juan.perez@company.com | 15/04/2023
ID 003 | Carlos Sanchez | csanchez@hotmail.com | 22/08/2023
ID 005 | Ana Maria | [BLANK] | 01/01/2024
ID 006 | Luis | luis@outlook.com | [BLANK]
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GPT-5.5
Turn ChatGPT into a Senior Data Architect. This structured prompt uses the CRF framework to instantly audit, clean, and standardize raw text or CSV files. It detects semantic duplicates, fixes inconsistent formats, and generates a logic anomaly report without needing a single Excel formula or macro. Ideal for chaotic CRMs, mailing lists, and finances.
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Updated 3 weeks ago
