hpi-sam/ASE-GenAI
Nov 2024 – Jan 2025
2 Months active
Languages Used
MarkdownSQLJupyter NotebookPython
Technical Skills
Database QueryingLLM IntegrationSQLTechnical WritingBLEU ScoreData Analysis
Silvan Verhoeven
PROFILE
Silvan Verhoeven
Silvan Verhoeven contributed to the hpi-sam/ASE-GenAI repository by developing core data analysis and evaluation tooling over a two-month period. He built a SQL Query Suite with execution plans, enabling structured data retrieval and reproducible analysis workflows, and documented the rationale behind LLM-assisted query generation. In a subsequent feature, Silvan implemented an evaluation framework to compare LLM-generated answers with human references using BLEU and ROUGE metrics, providing actionable insights into answer quality. His work leveraged Python, SQL, and libraries such as Pandas and NLTK, demonstrating depth in both technical implementation and reflective documentation for maintainable, data-driven development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
4,010
Activity Months2
Your Network
19 peopleSame Organization
@student.hpi.de1
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for hpi-sam/ASE-GenAI: Delivered a new evaluation framework (Task 2) to compare LLM-generated answers against human references using BLEU and ROUGE metrics. This framework provides measurable insights into answer quality and informs targeted improvements. No major bugs fixed this month. The work enhances benchmarking, user trust, and maintainability for ASE-GenAI.
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for hpi-sam/ASE-GenAI: Delivered a new evaluation framework (Task 2) to compare LLM-generated answers against human references using BLEU and ROUGE metrics. This framework provides measurable insights into answer quality and informs targeted improvements. No major bugs fixed this month. The work enhances benchmarking, user trust, and maintainability for ASE-GenAI.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Focused on delivering core data-analysis tooling for ASE-GenAI. Key feature delivered: Mini Project 1: SQL Query Suite and Execution Plans, introducing SQL query files and reflection documents to support structured data retrieval, analysis queries, and evaluation of development choices (including LLM-assisted generation and optimization) with explicit execution plans. This work was committed as 73f76e4e45bda3ed7947e7b51220cb74af5775ff (submission(mp1): Paul Chevelev, Silvan Verhoeven). Major bugs fixed: none reported this period. Overall impact: provides a reproducible, well-documented foundation for SQL-driven experimentation in the project, enabling rigorous data retrieval, analysis, and optimization workflows. Technologies/skills demonstrated: SQL, data retrieval/analysis, reflection and documentation practices, LLM-assisted query generation, execution planning, and collaborative code submission.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Focused on delivering core data-analysis tooling for ASE-GenAI. Key feature delivered: Mini Project 1: SQL Query Suite and Execution Plans, introducing SQL query files and reflection documents to support structured data retrieval, analysis queries, and evaluation of development choices (including LLM-assisted generation and optimization) with explicit execution plans. This work was committed as 73f76e4e45bda3ed7947e7b51220cb74af5775ff (submission(mp1): Paul Chevelev, Silvan Verhoeven). Major bugs fixed: none reported this period. Overall impact: provides a reproducible, well-documented foundation for SQL-driven experimentation in the project, enabling rigorous data retrieval, analysis, and optimization workflows. Technologies/skills demonstrated: SQL, data retrieval/analysis, reflection and documentation practices, LLM-assisted query generation, execution planning, and collaborative code submission.
Activity
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Quality Metrics
Correctness70.0%
Maintainability70.0%
Architecture70.0%
Performance70.0%
AI Usage70.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonSQL
Technical Skills
BLEU ScoreData AnalysisDatabase QueryingLLM IntegrationMachine Learning EvaluationNLTKNatural Language ProcessingPandasPythonROUGE ScoreSQLScikit-learnTechnical Writing
Repositories Contributed To
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