Noe Achache - RAG for a medical company: the technical and product challenges | Pydata London 2024

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RAG (Retrieval Augmented Generation) is the process of querying a (large) set of documents with natural language, leveraging vector search and llms. While it has recently become widely accessible to develop a Proof-Of-Concept RAG using OpenAI and one of the various open-source contributions (e.g. langchain), building a performant RAG that brings value to users is challenging.
This talk will focus on learnings from building a RAG for a medical company, to allow doctors to query drug documentation with natural language, using tools like Chainlit, Qdrant and Langsmith.
Naturally, a product question emerged: how to effectively leverage LLMs that can never guarantee 100% accuracy in the health sector?
We will explain how we addressed this challenge, as well as the various technical improvements implemented to enhance both the retrieval (vector search) and generation (llm) metrics of our RAG.

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