opea-project/GenAIEval
Nov 2024 – Nov 2024
1 Month active
Languages Used
MarkdownPython
Technical Skills
AI EvaluationDocumentationLLM EvaluationNatural Language ProcessingPythonRAG
Aasavari Kakne
PROFILE
Aasavari Kakne
Worked on the GenAIEval repository to deliver two new evaluation metrics for retrieval-augmented generation (RAG) systems, focusing on improving the fidelity of context assessment. Developed the Context Relevance and Context Recall metrics using Python, enabling more granular evaluation of how well retrieved information supports ground truth. Enhanced the project’s documentation in Markdown to clarify metric definitions and streamline onboarding for future contributors. Prioritized feature development and maintainability, ensuring a clean separation between metric logic and usage guidelines. The work advanced AI evaluation capabilities and supported faster iteration cycles, with no major bugs reported during the development period and all efforts focused on feature delivery.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
133
Activity Months1
Your Network
2129 peopleSame Organization
@intel.com2109
Work History
November 2024
3 Commits • 3 Features
Nov 1, 2024November 2024 performance highlights for GenAIEval (opea-project/GenAIEval): Delivered two new evaluation metrics to strengthen RAG-based assessment and improved documentation to accelerate adoption and maintainability. The work drives higher fidelity in retrieval-grounding evaluation, enabling better product decisions and faster iteration. Key deliverables focused on: (1) Context Relevance metric for RAGAAF, (2) Context Recall metric for Ragaaf, (3) README and documentation refinements. No major customer-facing bugs reported this month; effort prioritized feature delivery and documentation quality.
3 Commits • 3 Features
Nov 1, 2024November 2024 performance highlights for GenAIEval (opea-project/GenAIEval): Delivered two new evaluation metrics to strengthen RAG-based assessment and improved documentation to accelerate adoption and maintainability. The work drives higher fidelity in retrieval-grounding evaluation, enabling better product decisions and faster iteration. Key deliverables focused on: (1) Context Relevance metric for RAGAAF, (2) Context Recall metric for Ragaaf, (3) README and documentation refinements. No major customer-facing bugs reported this month; effort prioritized feature delivery and documentation quality.
November 2024
Activity
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Quality Metrics
Correctness96.6%
Maintainability93.4%
Architecture93.4%
Performance90.0%
AI Usage33.4%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
AI EvaluationDocumentationLLM EvaluationNatural Language ProcessingPythonRAGSoftware Developmentdocumentation
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