.NET samples demonstrating how to use AI in your .NET applications.
AI Samples for .NET
Welcome to the official home for .NET samples demonstrating how to use AI in your .NET applications. If you're new to AI, start at the top and work your way down. Otherwise, jump in wherever suits your interests.
Build 2024 - Infusing your .NET Apps with AI: Practical Tools and Techniques
Discover how to bring AI into your .NET application! This session covers the tools, libraries, and best practices for incorporating LLMs or other AI capabilities to create an "intelligent app". We'll explore practical examples, including how to leverage Azure AI services and the .NET AI ecosystem, to enhance your apps with AI.
Describe the issue:
Word spelling error. Hike Images samples (link1, link2) in the Semantic Kernel and Azure OpenAI SDK directories have this issue. Suggestion:
Change to dall.
Describe the issue:
Grammatical errors.
All samples in the Semantic Kernel and Azure OpenAI SDK directories have this issue. Suggestion:
The sample(s) should focus on only basic chat with a web UI, which can serve as a starting point template for future samples or new exploratory projects.
With this, we should provide a few different options for front-end UIs:
Today, the Python Evaluation building block can be used against a .NET backend that uses the Chat Protocol (Azure Search supports this). However, we know from customer feedback (A top request from MVPs) that they want a .NET native solution for this. The goal is to deliver .NET-specific building block documentation and sample code to support this. Currently this is still in a design phase, but the latest thinking is that we may adapt a community DotNet LLM Eval sample that was built by a fellow Microsoft employee (Maho Pacheco).
Work to be done:
• Evaluate options for LLM evaluation and land on the best practice guidance we’d give to devs. Decide if we’ll lead with the Python implementation for Build.
• Continue investigating what is needed for a .NET native evaluation block
• If continuing with adapting the DotNet LLM Eval sample, we’d need to:
o Likely provide an analysis UI that isn’t Grafana, for now aiming for parity with Pamela’s output.
o Figure out a solution for generating questions and answers to use as “grounded truth” – currently the Azure AI synthetics library, used by the Python block, does not have a .NET equivalent.
o Update to Support Semantic Kernel 1.x.
• Create guidance documentation