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Q&A Demo: GVHD Data

Evaluating and debugging a Q&A application, step by step

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Use Case Background

The data in this tutorial originates from a classic Retrieval Augmented Generation bot that answers questions about the GVHD medical condition. We're evaluating a GPT-3.5 LLM-based app that uses FAISS for the retrieval embedding vectors.
The knowledge base is built from a collection of online resources about the GVHD condition, and fictional data generated by chatGPT about a GVHD institute that provides medical support and appointments.
The differences between the two evaluated versions arise from the amount of data provided to the RAG application. Version v2 was given more information, resulting in better performance. Therefore, the second version was then deployed to production.

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Structure of this Example