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
Aasavari Kakne developed and integrated two new evaluation metrics for the opea-project/GenAIEval repository, focusing on enhancing retrieval-augmented generation (RAG) assessment. Using Python and natural language processing techniques, Aasavari implemented a context relevance metric and a context recall metric, each with detailed scoring rubrics and prompt templates to support nuanced evaluation of retrieval-grounding. The work included refining documentation in Markdown to improve clarity and maintainability, ensuring that metric definitions and usage guidelines were clearly separated for easier onboarding and future extension. This engineering effort prioritized feature depth and documentation quality, supporting more reliable and actionable LLM evaluation workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
133
Activity Months1
Your Network
1980 people
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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