talk-data.com

Join Pamela Fox, Burke Holland, Bruno Capuano, and Jon Galloway for a fast-paced showcase of GPT-5 integration across Microsoft developer tools, featuring practical demos, code samples, and insights for Python, .NET, JavaScript, and more.

Join Pamela Fox, Burke Holland, Bruno Capuano, and Jon Galloway for a fast-paced showcase of GPT-5 integration across Microsoft developer tools, featuring practical demos, code samples, and insights for Python, .NET, JavaScript, and more.

Join Pamela Fox, Burke Holland, Bruno Capuano, and Jon Galloway for a fast-paced showcase of GPT-5 integration across Microsoft developer tools, featuring practical demos, code samples, and insights for Python, .NET, JavaScript, and more.

Join us for Episode 07 of Season 1 of #ModelMondays as we put the spotlight on open-source and AI development with a look at the Meta #Llama4 herd of recently released models. Huge props to Alexander Hughes for giving us an introduction to the Llama4 capabilities and to Pamela Fox for a hands-on look at multimodal features and more.

• https://aka.ms/model-mondays - Explore All Resources
• https://aka.ms/model-mondays/chat - Continue The Conversation
• https://aka.ms/model-mondays/collection - Explore The Model Cards
• https://aka.ms/model-mondays/newsletter - Get The Weekly Recaps

RAG (Retrieval Augmented Generation) is the most popular approach used to get LLMs to answer user questions grounded in a domain. How can you be sure that the answers are accurate, clear, and well formatted? Evaluation! In this session, we'll show you how to use Azure AI Studio and the Promptflow SDK to generate synthetic data and run bulk evaluations on your RAG app. Learn about different GPT metrics like groundedness and fluency, and consider other ways you can measure the quality of your RAG app answers.

Presented by Nitya Narasimhan, AI Advocate, and Pamela Fox, Python Advocate

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

**📌 Check out the RAGHack 2024 series here! **

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most popular approach used to get LLMs to answer user questions grounded in a domain. How can you be sure that the answers are accurate, clear, and well formatted? Evaluation! In this session, we'll show you how to use Azure AI Studio and the Promptflow SDK to generate synthetic data and run bulk evaluations on your RAG app. Learn about different GPT metrics like groundedness and fluency, and consider other ways you can measure the quality of your RAG app answers.

Presented by Nitya Narasimhan, AI Advocate, and Pamela Fox, Python Advocate

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

**📌 Check out the RAGHack 2024 series here! **

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

If you're trying to get an LLM to accurately answer questions about your own documents, you need RAG: Retrieval Augmented Generation. With a RAG approach, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2.

Presented by Matt Gotteiner, Product Manager for Azure AI Search, and Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is a way to get LLMs to answer questions grounded in a particular knowledge base. What do you do when your knowledge base includes images, like graphs or photos? You first need to generate embeddings using a multimodal model, like the one available from Azure Computer Vision, search those embeddings using a powerful vector search like Azure AI Search, and then send any retrieved text and images to a multimodal LLM like GPT-4o. Learn how to get started quickly with a RAG on multimodal documents in this session.

Presented by Pamela Fox, Python Advocate at Microsoft

** Part of RAGHack, a free global hackathon to developer RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Discover the many ways you can build RAG applications on top of Azure Database for PostgreSQL Flexible Servers. We'll start by using the pgvector extension for vector similarity search. Then we'll explore the new azure_ai extension which provides built-in functions for embeddings, summarization, sentiment analysis, and more. Finally, we'll demo the new azure_local_ai extension to efficiently run pre-trained models on our database server, which can be a great fit for RAG applications that require custom embedding models.

Presented by Joshua Johnson, Principal TPM on Azure PostgreSQL, and Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Azure AI Search is a powerful search engine, with many features designed specifically for RAG applications. We'll demonstrate optimal retrieval using hybrid search with the semantic ranker, show the new integrated vectorization feature for cloud-based data ingestion, and discuss vector storage optimization.

Presented by Farzad Sunavala, Product Manager on Azure AI Search, and Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Azure AI Search is a powerful search engine, with many features designed specifically for RAG applications. We'll demonstrate optimal retrieval using hybrid search with the semantic ranker, show the new integrated vectorization feature for cloud-based data ingestion, and discuss vector storage optimization.

Presented by Farzad Sunavala, Product Manager on Azure AI Search, and Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Azure AI Search is a powerful search engine, with many features designed specifically for RAG applications. We'll demonstrate optimal retrieval using hybrid search with the semantic ranker, show the new integrated vectorization feature for cloud-based data ingestion, and discuss vector storage optimization.

Presented by Farzad Sunavala, Product Manager on Azure AI Search, and Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to develop apps using RAG with Python and the OpenAI SDK. We'll walk through our most popular RAG solution, showing the process of data ingestion with Azure Document Intelligence and AI Search, then walking through the RAG steps of query rewriting, hybrid search, and question answering. You'll learn how to easily bring your own data into the RAG solution, and how to customize the prompts and UI for your domain.

Presented by Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!