RAGChat: Evaluating RAG answer quality

In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat How can you be sure that the RAG chat app answers are accurate, clear, and well formatted? Evaluation! In this session, we'll show you how to generate synthetic data and run bulk evaluations on your RAG app, using the azure-ai-evaluation SDK. Learn about GPT metrics like groundedness and fluency, and custom metrics like citation matching. Plus, discover how you can run evaluations on CI/CD, to easily verify that new changes don't introduce quality regressions. This session is a part of a series! To learn more, click here: https://aka.ms/RAGDeepDive 📌 Get more RAG resources: https://aka.ms/thesource/github/yt #MicrosoftReactor #learnconnectbuild #RAGDeepDive [eventID:24578]

RAGChat: Evaluating RAG answer quality

Microsoft Reactor

RAGChat: Evaluating RAG answer quality

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Microsoft Reactor

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RAGChat: Private deployment

Building RAG Solutions with Azure AI Foundry

Find the BEST RAG Strategy with Domain Specific Evals

Contributing To Open Source – Beginner's Guide

Webinar: Unstructured 101

Mastering Semantic Classification with Embeddings and Vector Similarity in .NET/C#

Feed Your OWN Documents to a Local Large Language Model!

Building Production-Ready RAG Applications: Jerry Liu

YAAP | RAG Evaluation - You're Doing It Wrong

Different Evaluation Methodologies For Preventing LLM Failures

Using the Well-Architected Framework

Intro and demo to RAG with Azure OpenAI

How Azure AI Search Enables Retrieval-Augmented Generation (RAG) at Scale

RAGChat: The RAG solution for Azure

Data Access Control for AI RAG Apps on Azure

Azure AI Search: RAG for better results larger scale faster answers | BRK105

RAG 101: Retrieval Augmented Generation

Advanced RAG with Azure AI Search - top questions answered | BRK100

How to use Microsoft Access - Beginner Tutorial

Master Autonomous AI Agents in Microsoft Copilot Studio - Easy to Build & Extremely Powerful