
Prajjwal Yadav developed end-to-end Retrieval Augmented Generation (RAG) demonstrations for the weaviate/recipes repository, focusing on enhancing customer-facing Jupyter notebooks and streamlining onboarding. Using Python and the Weaviate client, Prajjwal integrated OpenAI models, including gpt-5 and gpt-oss:20b via Ollama, to showcase RAG workflows with improved data loading and setup instructions. The work included refactoring the OpenAI integration to fetch data from URLs and simplifying dependencies, which reduced friction for new contributors. By updating the RAG fashion email generation notebook, Prajjwal enabled personalized recommendations, demonstrating depth in generative AI, API integration, and maintainable notebook development practices.
In August 2025, delivered end-to-end RAG-driven demonstrations in the weaviate/recipes repository, improved OpenAI integration reliability, and streamlined data loading to accelerate experimentation and onboarding. The work enhances customer-facing notebooks and reduces setup friction while expanding model compatibility.
In August 2025, delivered end-to-end RAG-driven demonstrations in the weaviate/recipes repository, improved OpenAI integration reliability, and streamlined data loading to accelerate experimentation and onboarding. The work enhances customer-facing notebooks and reduces setup friction while expanding model compatibility.

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