From the course: Building in Azure AI Foundry
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Evaluate your AI application
From the course: Building in Azure AI Foundry
Evaluate your AI application
- [Instructor] After developing your AI solution, you need to evaluate its performance. A typical evaluation involves several key elements. We need a test dataset as the input. We need to assess the changes in different areas, such as models, model parameters, prompts, system messages, flows, and your data. We also need to apply various evaluation metrics, such as relevance, coherence, and groundedness. Because there are so many factors in an evaluation, a simple test like entering some prompts in the chat is insufficient and time consuming. We need to use an evaluation tool to help us. In Azure AI Foundry, we can use the tool to run two types of evaluations. Manual evaluations for manually reviewing the results after generating AI responses with your test datasets. Metric evaluations for evaluating the performance of your AI application using industry standard metrics. Now let's do a quick demo. Here's my demo project in the…
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