AI Prompt to Segment Customer Data Into Meaningful Groups

Extract the following fields from the text below: [FIELD 1, FIELD 2, FIELD 3, FIELD 4].

Output rules:
- Return strictly a JSON array of objects, one per entity found.
- Use exactly these keys: [key_1, key_2, key_3, key_4].
- If a value is missing or unclear, use null. Never guess.
- Do not wrap the output in markdown code fences.
- Do not add any explanation before or after the JSON.

Text:
"""
[PASTE TEXT]
"""

ROLE: You are a data cleaning assistant helping standardize a spreadsheet dataset for analysis.

CONTEXT: Below is a sample of the dataset. Columns are: [LIST OF COLUMN NAMES]

[PASTE SAMPLE DATA HERE, INCLUDING KNOWN PROBLEM ROWS]

TASK:
1. Review each column and identify formatting inconsistencies (e.g. date formats, capitalization, whitespace, abbreviations, units)
2. Propose and apply a single standard format for each column: [SPECIFY TARGET FORMATS WHERE KNOWN, e.g. dates as YYYY-MM-DD]
3. Flag rows that look like duplicates or outliers, but do not delete them — mark them for my review instead
4. Do not alter these columns without flagging first: [COLUMNS REQUIRING REVIEW BEFORE CHANGES, e.g. customer name, ID numbers]

CONSTRAINTS:
- Preserve every original row unless I confirm a deletion
- Do not invent or infer missing values — leave them blank and flag them
- [ANY ADDITIONAL CONSTRAINT, e.g. keep a specific column's original casing]

OUTPUT FORMAT:
1. The cleaned dataset as a table
2. A change log listing each column, what inconsistency was found, and what standard was applied
3. A separate list of flagged rows (possible duplicates/outliers) with a one-line reason for each flag

Role: You are a data quality analyst reviewing a dataset for duplicate records.

Context:
- Dataset: [PASTE DATASET OR DESCRIBE FILE AND COLUMNS]
- Matching key(s): [FIELDS THAT SHOULD BE TREATED AS THE PRIMARY MATCH, e.g. EMAIL AND LAST NAME]
- Secondary fields to consider for near-matches: [E.G. PHONE NUMBER, COMPANY NAME, ADDRESS]
- Known formatting inconsistencies: [E.G. SOME ENTRIES HAVE EXTRA SPACES, MIXED CASE, ABBREVIATIONS]

Task:
1. Identify rows that are exact duplicates on the matching key(s).
2. Identify rows that are likely duplicates despite minor formatting differences (case, spacing, abbreviation, punctuation).
3. For each duplicate group, recommend which row to keep based on: [RULE, e.g. MOST RECENT DATE, MOST COMPLETE RECORD].

Constraints:
- Do not delete or merge any rows yourself — only recommend.
- Do not treat records as duplicates solely because one field matches if other key fields clearly conflict.
- Flag anything uncertain as "possible duplicate" rather than guessing.

Output format:
A table with columns: Row ID(s) involved | Match type (exact / likely / possible) | Matching fields | Recommended row to keep | Reason.
After the table, list any rows you could not classify confidently and why.