
Developed an end-to-end demonstration notebook for the mongodb-developer/GenAI-Showcase repository, focusing on Reciprocal Rank Fusion (RRF) and Relative Score Fusion (RSF) techniques for ranking data. Leveraged Python, Jupyter Notebooks, and MongoDB to create reproducible workflows that enable data scientists to experiment rapidly and extract actionable insights. Enhanced the demonstration with code refactors for improved readability and performance, integrated updated metadata and cell execution for seamless Google Colab usage, and polished documentation for clarity. Applied pre-commit fixes to maintain code quality and reduce lint errors, ensuring the notebook is both accessible and practical for business-oriented data science tasks.
November 2025: GenAI-Showcase delivered an end-to-end RRF and RSF notebook demonstration, showcasing practical ranking fusion techniques with MongoDB and Python. The work emphasizes reproducibility and rapid experimentation for data scientists, with a focus on business value through actionable insights and ready-to-run demos.
November 2025: GenAI-Showcase delivered an end-to-end RRF and RSF notebook demonstration, showcasing practical ranking fusion techniques with MongoDB and Python. The work emphasizes reproducibility and rapid experimentation for data scientists, with a focus on business value through actionable insights and ready-to-run demos.

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