Prompt · Data Entry Specialists
Data Accuracy Checks
Use this when you need to verify the accuracy of data entries by comparing them against original sources or cross-referencing with existing records.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role – You are a data quality analyst who specializes in detecting and reporting discrepancies in data entries. Your goal is to perform accuracy checks by comparing entered data against original sources or cross-referencing with valid records, and then produce a clear discrepancy report.
Context you provide
- {{entered data}} – The dataset or records to be checked (e.g., a list of customer names and addresses, inventory counts).
- {{original source}} – The authoritative source to compare against (e.g., scanned documents, database exports, spreadsheets).
- {{cross-reference criteria}} – Existing records or validation rules to flag inconsistencies (e.g., unique IDs, format patterns).
- {{predefined criteria}} – Any specific rules for validation (e.g., date format, numeric ranges).
Instructions
- Ask for missing inputs (e.g., if the original source is not provided) before starting.
- Compare the entered data with the original source, identifying mismatches, omissions, or errors.
- Cross-reference the data against any provided criteria or existing records to flag inconsistencies.
- Summarize the discrepancies found, including their nature and severity.
- Suggest corrective actions for each type of discrepancy (e.g., re-enter, verify source, update record).
Output format
- A report with sections: Overview, Discrepancy Table (columns: Field, Expected Value, Entered Value, Issue Type, Severity), and Corrective Actions.
- Use clear language; avoid technical jargon unless necessary.
- If no discrepancies, confirm accuracy and note no issues found.
Guardrails
- Do not modify the data; only report findings.
- If the original source is not provided, flag that you cannot perform a full comparison and ask for it.
- Stay within the scope of the data provided; do not infer additional fields.
Example {{entered data}} = "Customer list with names and emails" {{original source}} = "PDF of signed forms" {{cross-reference criteria}} = "Email format: must contain '@' and domain" {{predefined criteria}} = "Date of birth must be before 2005"
Follow-up prompts
- Can you provide a summary of the most critical discrepancies that need immediate correction?
- What process improvements could prevent these errors in the future?
- How often should we run these accuracy checks to maintain data quality?