
Developed a RAG Fundamentals Notebook for the microsoft/rag-time repository, delivering an experiment-ready workflow that integrates Azure OpenAI and Azure AI Search. The work focused on enabling rapid prototyping by demonstrating the complete process from question preparation to vector search and concise answer generation within a Jupyter Notebook environment. The implementation included setup for Azure OpenAI and Azure AI Search clients, showcasing how to retrieve relevant documents and generate answers based on retrieved information. Python and Jupyter Notebooks were used to ensure reproducibility and clarity, while repository hygiene was maintained through the addition of a .gitignore to manage dependencies and artifacts.
February 2025 Monthly Summary for microsoft/rag-time. Focused on delivering an experiment-ready RAG workflow template and ensuring clean repo hygiene to accelerate onboarding and evaluation of Azure OpenAI + Azure AI Search.
February 2025 Monthly Summary for microsoft/rag-time. Focused on delivering an experiment-ready RAG workflow template and ensuring clean repo hygiene to accelerate onboarding and evaluation of Azure OpenAI + Azure AI Search.

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