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Metric-Driven Development of a RAG system

• Luxin Zhang
• 14 Min To Read
• 12 Jan, 2024
• • Software engineering

Prerequisites

• Basic knowledge of pros and cons of LLM APIs
• Preliminary knowledge of RAG structure

Introduction

Retrieval Augmented Generation (RAG) is commonly used to enhance the performance of LLM-based systems or incorporate domain-specific knowledge to improve the generated answers of the LLM. However, effectively evaluating and tracking the performance of a RAG system is not straightforward, posing a real challenge in the development and monitoring of such systems in a production environment.

My feedback after upgrading a Quarkus project from v2 to v3

• Yassine Benabbas
• 6 Min To Read
• 26 Dec, 2023
• • Development and programming

In our team (Worldline TechSquad), we develop and maintain a CRUD REST API for managing our internal events. We use Kotlin and Quarkus as our base language and framework respectively. The project started at the time with Quarkus 1 which was upgraded to Quarkus 2. Since July 2023 Quarkus 3.2 is the current LTS release . Thus, we wanted to update our app as soon as possible to remain on a proper long term support version.

How Open is Generative AI? Part 2

• Raphaël Semeteys Luxin Zhang
• 20 Min To Read
• 19 Dec, 2023
• • Artificial intelligence and data science

Embarking on the second and last part of our ‘Generative AI Openness’ series, we earlier established a straightforward framework to gauge the openness of Large Language Models (LLMs) and utilized it to explore LLM development and the positioning of key players. We noticed a trend towards increasingly restricted LLM artifacts for OpenAI and Google, contrasted with Meta’s more open approach.

How to do some performance tests : Defining your test strategy

• Yann Couturier
• 6 Min To Read
• 08 Nov, 2023
• • Software engineering

Why do I need a test strategy?

Having your performance test running in the best conditions implies a number of steps, material and immaterial means, and people in order to obtain the results which cover the objectives.

Naming colors: overcoming your 50 shades of gray

• Simon Mercier
• 5 Min To Read
• 31 Oct, 2023
• • Development and programming

Naming colors correctly in my code has always been a headache. I was familiar with two schools of thought:

How Open is Generative AI? Part 1

• Raphaël Semeteys Luxin Zhang
• 18 Min To Read
• 25 Oct, 2023
• • Artificial intelligence and data science

Welcome to this two-part series on Generative AI Openness, where we explore the history, current landscape, and potential future of open collaboration and proprietary control in the development of Language Language Models (LLMs). In this first part, we will delve into the importance of inspecting the openness of each component in the LLM training process, and how this can impact the potential limitations on the model use or reuse imposed by one or more of its components. In the second part of this series, we will explore the potential benefits and drawbacks of sharing Generative AIs openly for the collective advancement of society.