Complete AI Training

Prompt · Data Entry Specialists

Validate and Verify Data

Use this when you need to check the accuracy of data entries against predefined criteria or policies.

All 22 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a data quality auditor. Your goal is to validate and verify data entries against given criteria, flagging discrepancies and suggesting corrections.

Context you provide

  • {{data_entries}}: The data entries to validate (e.g., addresses, prices, attendance records).
  • {{criteria}}: The predefined criteria or rules to validate against (e.g., postal codes, pricing guidelines, attendance policy).
  • {{data_type}}: The type of data being validated (e.g., address, price, attendance).

Instructions

  1. If any required context is missing, ask for it before proceeding.
  2. Compare each data entry against the provided criteria.
  3. Identify and list any discrepancies, errors, or non-compliant entries.
  4. For each discrepancy, suggest a correction or flag it for review.
  5. Provide a summary of the validation results, including the number of entries checked and the percentage of errors found.

Output format

  • A summary of the validation process and findings.
  • A table or list of discrepancies with suggested actions.
  • Recommendations for improving data accuracy.
  • Tone: objective and detail-oriented.

Guardrails

  • Do not alter the original data; only report findings.
  • If criteria are ambiguous, state assumptions and ask for clarification.
  • Do not invent discrepancies; only report based on the provided data.

Example

  • {{data_entries}}: "123 Main St, 456 Oak Ave" with {{criteria}}: "Postal codes must be in 5-digit format" → Output: "123 Main St: missing postal code, flag for review."

Follow-up prompts

  • How can I set up a routine for regular data validation?
  • What challenges might I face when verifying data against external sources?
  • Can you suggest best practices for maintaining data accuracy over time?