🌟 Session Overview 🌟

Session Name: AI Development Lifecycle: Learnings of What Changed with LLMs Speaker: Noe Achache Session Description: When comparing the building of models based on LLMs (Large Language Models) versus more traditional machine/deep learning approaches, two observations are drawn:

Building Proof of Concepts has become incredibly easy. Evaluation is much more challenging. As a result, the evaluation step is often neglected, leading to pointless iterations and a lack of knowledge on the true performance of the product. This is one of the main obstacles to moving into production, especially in circumstances where we need high accuracy of the results.

In this talk, we will explore the lessons learned from building products that are typical use cases of these technologies, such as financial document analysis automation and a RAG (Retrieval Augmented Generation) tool for the drug documentation of a medical company.

We will mostly focus on the essential steps of the development workflow that are often overlooked: dataset collection, evaluation, and monitoring. New tools such as langsmith for monitoring or promptfoo for manual/automatic evaluation have been released to ease the implementation of these best practices in the context of LLMs. 🚀 About Big Data and RPA 2024 🚀

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