AI Tour BRK450 - Prompty, AI Studio and practical E2E development.
This repo is intended for resources of the breakout session: Prompty, AI Studio and practical E2E development.
Session Desciption
Integrating GenAI into traditional development can be daunting. Prompty simplifies LLM app development and works seamlessly with tools like Visual Studio Code and GitHub. Learn how to build multi-model LLM (agent) architecture, evaluation, deployment, and monitoring.
Duration
45 Minutes
Slide Deck
Learning Outcomes
By the end of this session you will understand:
- How to get started with LLMs
- Understand the Prompty Specification
- How to get started with Prompty in Visual Studio Code
- How to develop an App with Prompty
Technology Used
Additional Resources and Continued Learning
| Resources | Links | Description |
|---|---|---|
| Prompty Documentation | Prompty | Learn more about Prompty |
| Azure AI Samples | Azure AI Samples | Learn more about how you can build Prompty projects |
Content Owners
Responsible AI
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.
The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. We also have an interactive Content Safety Studio that allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.
Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using generation quality and risk and safety metrics.
You can evaluate your AI application in your development environment using the prompt flow SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the prompt flow sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Studio.