📅 2024-09-17 — Session: Developed QA Testing Workflow for NoSQL Data
🕒 13:25–14:10
🏷️ Labels: QA, Nosql, Python, Data Validation, Automation
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to develop and refine a QA testing workflow for NoSQL data, focusing on validating parsed data against original text to ensure accuracy and consistency.
Key Activities
- Prepared memos for the development team to finalize key points and provide feedback.
- Summarized progress on schema parsing and data processing, outlining challenges and next steps.
- Developed a Jupyter notebook structure for QA testing of NoSQL data.
- Outlined a QA testing workflow using Python for processing NoSQL data.
- Created a Python function to compare parsed data fields with original text.
- Developed a dynamic function for comprehensive QA comparison of parsed data.
- Updated AI-driven QA workflow using OpenAI to compare PDF text with NoSQL data.
- Refined AI prompt for legal document QA in Spanish.
- Implemented iterative file saving in Python for QA results.
Achievements
- Established a comprehensive QA testing workflow for NoSQL data.
- Developed dynamic and AI-driven functions for data validation.
- Improved efficiency in data processing and error handling.
Pending Tasks
- Implement the refined AI prompt in production.
- Conduct further testing on the QA workflow with real datasets.