Promptflow

Creating and scaling your own custom copilots with Azure AI Studio: Hanchi Wang

Feb 6, 2025 · 24:21

Hanchi Wang, Software Engineer Lead at Azure AI, introduces Azure AI Studio and Promptflow for creating and scaling custom copilots, focusing on tracing, evaluation, and monitoring. He demonstrates a chatbot app that uses the Assistant API with a sales data insight tool (natural language to SQL) and a code interpreter. With Promptflow's trace decorator, developers capture inputs, outputs, and LLM interactions, viewable in a local UI and shareable via Azure AI Studio. For evaluation, he shows synthetic test data sets, content safety evaluators, and custom evaluators like execution time, error rate, and SQL similarity, comparing models such as GPT-4 Turbo, Mistral large, and Phi-3. In production, monitoring dashboards in Application Insights track model duration, token usage (prompt vs. completion), and failure rates, enabling engineers to optimize performance and cost.

Running AI Application in Minutes w/ AI Templates: Gabriela de Queiroz, Pamela Fox, Harald Kirschner

Aug 14, 2024 · 1:29:02

In this workshop, Microsoft's Gabriela de Queiroz, Pamela Fox, and Harald Kirschner show how to deploy AI applications in minutes using AI templates, Azure OpenAI proxy, and GitHub Codespaces. They walk through deploying a simple chat app, then two RAG applications: one that queries a Postgres database with SQL filtering, and another that performs RAG on unstructured documents using Azure AI Search. Key decisions include using async frameworks like Quart and FastAPI, token-based chunking, and hybrid retrieval with semantic reranking for best results. They stress the importance of running evaluations with hundreds of samples and share production insights from Copilot Chat, where TFIDF sparse indexing and LLM reranking are used. The session includes free Azure credits and a proxy to bypass Azure OpenAI approval, allowing attendees to deploy everything without spending their own money.