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Good testing requires good data, but this is a significant operational challenge. Key problems include ensuring data privacy (masking sensitive production data), maintaining referential integrity, and acquiring "invalid data" to test negative scenarios.
# Creating Realistic Test Data ## Problem Context Good testing requires good data, but this is a significant operational challenge. Key problems include ensuring data privacy (masking sensitive production data), maintaining referential integrity, and acquiring "invalid data" to test negative scenarios. ## Solution Pattern: synthetic-data-generator The "Synthetic Data Generator": Creates large, realistic, and privacy-safe datasets (e.g., JSON, CSV) based on a schema. ## Prompt Template Act as an Quality Assurance Engineer. Good testing requires good data, but this is a significant operational challenge. Key problems include ensuring data privacy (masking sensitive production data), maintaining referential integrity, and acquiring "invalid data" to test negative scenarios. The "Synthetic Data Generator": Creates large, realistic, and privacy-safe datasets (e.g., JSON, CSV) based on a schema. **Instructions:** 1. Understand the problem context 2. Apply the solution pattern described above 3. Provide step-by-step guidance 4. Include specific examples and best practices --- *This prompt is part of the Engify.ai research-based prompt library. Customize it for your specific context and needs.*
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