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Microsoft.Extensions.AI–Part VIII–Evaluations

Back from holiday with charged batteries, we continue our journey exploring the Microsoft.Extensions.AI library. Today we have a look at evaluating AI models.

This post is part of a blog series. Other posts so far:

What is Microsoft.Extensions.AI.Evaluation?

Microsoft.Extensions.AI.Evaluation is a set of libraries with one common goal; simplifying the process of evaluating the quality and accuracy of responses generated by AI models. Measuring the quality of your AI apps is challenging, you need to evaluate metrics like:

  • Relevance: How effective is the response for a given prompt?
  • Truthfulness: Is the response factually correct?
  • Coherence: Is the response logically structured and consistent?
  • Completeness: Is the response a sufficient answer?
  • And many more…

The evaluation libraries handle this for you through a list of available evaluators that can easily be integrated in your existing test infrastructure and framework.

But enough talking, let’s give it a try…

Integrate AI quality validation for our chat application

Start by adding a new test project to your solution using the framework of your choice. I'll be using XUnit in this post but the library itself is completely test framework agnostic.

Add a reference to the Microsoft.Extensions.AI.Evaluation.Quality library:

dotnet package add Microsoft.Extensions.AI.Evaluation.Quality

Now we first need to bootstrap our ChatConfiguration:

And also build up our prompt:

Once these 2 things are in place, we can setup the evaluator(s) for our test, invoke our LLM and evaluate the results:

Of course, this test fails as the AI suggested the moon as a good holiday location.

Tomorrow we further extend this example and have a look at caching of the model responses and reporting of the evaluations.

More information

The Microsoft.Extensions.AI.Evaluation libraries - .NET | Microsoft Learn

ai-samples/src/microsoft-extensions-ai-evaluation/api/README.md at main · dotnet/ai-samples

Exploring new Agent Quality and NLP evaluators for .NET AI applications - .NET Blog

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