fiddler-labs/fiddler-examples
Mar 2025 – Jul 2025
2 Months active
Languages Used
Jupyter NotebookMarkdownPythonJSON
Technical Skills
API IntegrationCode RefactoringData AnalysisData VisualizationDocumentationError Handling
Jennifer Flynn
PROFILE
Jennifer Flynn
Jenn contributed to the fiddler-labs/fiddler-examples repository by developing and enhancing Jupyter Notebook workflows that streamline onboarding and evaluation for LLM-based judgment and faithfulness guardrails. She implemented new notebooks for faithfulness scoring and latency visualization, improved prompt specification workflows, and refined data handling guidance. Using Python, Jupyter Notebooks, and API integration, Jenn focused on improving error handling, documentation clarity, and dataset sampling to accelerate experimentation and reduce support overhead. Her work reduced onboarding time, improved evaluation accuracy, and enabled faster decision-making for users, demonstrating depth in machine learning operations, prompt engineering, and technical writing within the context of LLM workflows.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
3
Lines of code
2,389
Activity Months2
Your Network
28 people
Work History
July 2025
7 Commits • 2 Features
Jul 1, 2025During 2025-07, shipped enhancements to Fiddler examples that strengthen LLM-based judgment workflows and streamline onboarding for new users. Delivered notebook-based enhancements for Fiddler LLM-as-a-Judge, including prompt specification workflows, example usage, and readability improvements, enabling faster evaluation cycles. Extended Quickstart notebooks with dataset reduction for faster onboarding, added a download-after-publish workflow, and updated LLM Prompt Spec notebook with prompts and data handling guidance. Fixed a Quickstart documentation label typo and clarified re-running guidance, reducing support overhead. Overall, these efforts improve evaluation accuracy, onboarding speed, and developer experience, aligning with business goals of faster time-to-value.
7 Commits • 2 Features
Jul 1, 2025During 2025-07, shipped enhancements to Fiddler examples that strengthen LLM-based judgment workflows and streamline onboarding for new users. Delivered notebook-based enhancements for Fiddler LLM-as-a-Judge, including prompt specification workflows, example usage, and readability improvements, enabling faster evaluation cycles. Extended Quickstart notebooks with dataset reduction for faster onboarding, added a download-after-publish workflow, and updated LLM Prompt Spec notebook with prompts and data handling guidance. Fixed a Quickstart documentation label typo and clarified re-running guidance, reducing support overhead. Overall, these efforts improve evaluation accuracy, onboarding speed, and developer experience, aligning with business goals of faster time-to-value.
July 2025
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for fiddler-labs/fiddler-examples focusing on business value and technical achievements. Delivered enhancements to the Faithfulness Guardrails Notebook suite to accelerate experimentation and onboarding for the Guardrails Free Trial, improved stability, and clarified documentation across notebooks.
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for fiddler-labs/fiddler-examples focusing on business value and technical achievements. Delivered enhancements to the Faithfulness Guardrails Notebook suite to accelerate experimentation and onboarding for the Guardrails Free Trial, improved stability, and clarified documentation across notebooks.
Activity
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Quality Metrics
Correctness91.6%
Maintainability91.6%
Architecture86.6%
Performance83.4%
AI Usage35.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPython
Technical Skills
AI ObservabilityAPI IntegrationCode RefactoringData AnalysisData EngineeringData SamplingData ScienceData VisualizationDocumentationError HandlingFiddler PlatformGenAIJupyter NotebookJupyter NotebooksLLM
Repositories Contributed To
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