📅 2024-09-17 — Session: Developed QA Testing Workflow for NoSQL Data
🕒 13:30–14:10
🏷️ Labels: QA, Nosql, Python, AI, Data Validation
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and refine a quality assurance (QA) testing workflow for NoSQL data, leveraging automation and AI-driven processes.
Key Activities
- Prepared a memo for the lead developer and project manager to finalize key points and assign QA tasks based on 50 parsed results.
- Summarized progress on schema parsing and data processing, outlining challenges and next steps for QA tasks involving AI agents.
- Outlined the structure for a Jupyter notebook to perform QA testing on extracted NoSQL data, focusing on field validation and discrepancy identification.
- Developed a Python workflow for QA testing of NoSQL data, emphasizing loading existing data and processing new resolutions.
- Created a Python function to compare parsed data fields with original text, specifically checking references and licitations.
- Provided a dynamic Python function for comprehensive QA comparison, handling lists, dictionaries, and simple values.
- Updated an AI-driven workflow for comparing PDF text with parsed NoSQL data, using OpenAI for feedback and improvement suggestions.
- Refined an AI prompt for legal document QA, focusing on necessary modifications and discrepancies.
- Implemented a Python code snippet for iterative file saving of QA results, ensuring data is appended with error handling and logging.
Achievements
- Successfully developed a comprehensive QA testing workflow for NoSQL data, integrating Python and AI tools.
- Finalized key points and feedback mechanisms for the development team.
Pending Tasks
- Further refinement of AI-driven QA processes and integration with existing systems.
- Continuous monitoring and improvement of the QA testing workflow based on feedback and results.