d-fine/DatalandQALab
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
API IntegrationAutomated TestingCloud Services (Azure)Data AnalysisData EngineeringData Extraction
jonkra
PROFILE
Jonkra
Jonas Kraemer developed end-to-end QA automation and data extraction workflows for the d-fine/DatalandQALab repository, focusing on EU Taxonomy data analysis. He implemented new Jupyter notebooks and Python backend logic to automate testing, data generation, and report submission, integrating data validation against PDF reports to streamline QA cycles. In the following month, Jonas introduced a template-driven prompting workflow using Azure OpenAI, building a PromptingService that generates structured prompts for document analysis and extracts data at scale. His work combined API integration, prompt engineering, and cloud services, delivering robust, maintainable solutions that improved clarity, efficiency, and scalability in QA processes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,592
Activity Months2
Your Network
13 peopleSame Organization
@stud.tu-darmstadt.de5
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12 | DatalandQALab Focused on delivering a template-driven prompting workflow for document analysis, enabling structured data extraction and scalable QA insights.
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12 | DatalandQALab Focused on delivering a template-driven prompting workflow for document analysis, enabling structured data extraction and scalable QA insights.
December 2024
November 2024
2 Commits • 1 Features
Nov 1, 20242024-11 Monthly summary for d-fine/DatalandQALab: Delivered end-to-end QA automation and data extraction/analysis workflow enhancements for EU Taxonomy data (Nuclear and Gas). Implemented new Jupyter notebooks for automated testing, data generation, and report submission, and backend changes to support automated data validation against PDF reports and direct QA report submission. Refactored and enhanced the data extraction/analysis workflow by updating notebooks and Python code, improved prompt instructions for yes/no responses, and added new code cells for processing extracted data to improve clarity and efficiency. These changes accelerate QA cycles, improve report reliability, and reduce manual effort in data validation and submission.
November 2024
2 Commits • 1 Features
Nov 1, 20242024-11 Monthly summary for d-fine/DatalandQALab: Delivered end-to-end QA automation and data extraction/analysis workflow enhancements for EU Taxonomy data (Nuclear and Gas). Implemented new Jupyter notebooks for automated testing, data generation, and report submission, and backend changes to support automated data validation against PDF reports and direct QA report submission. Refactored and enhanced the data extraction/analysis workflow by updating notebooks and Python code, improved prompt instructions for yes/no responses, and added new code cells for processing extracted data to improve clarity and efficiency. These changes accelerate QA cycles, improve report reliability, and reduce manual effort in data validation and submission.
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture76.6%
Performance73.4%
AI Usage53.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
API IntegrationAutomated TestingAzure OpenAIBackend DevelopmentCloud Services (Azure)Data AnalysisData EngineeringData ExtractionData ValidationJupyter NotebooksPrompt EngineeringPython Programming
Repositories Contributed To
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