DRAG DROP You manage an Azure Al Foundry project. You deploy a large language model from the model catalog. You need to manually evaluate the model, collect the statistics, and be able to review the results later.

To perform a manual evaluation in Azure AI Foundry, you first import data in CSV format (or JSONL) containing the test prompts into the Manual Evaluation interface. Once the model generates responses for these prompts, you provide thumbs up or down ratings to model responses to grade their quality (correctness, safety, etc.). Finally, you save the evaluation results to the project, allowing you to view statistics and compare performance over time.

Microsoft Learn: Evaluate generative AI apps with Azure AI Foundry

Context: Describes the manual evaluation workflow. "You can import stored Chat Completions, create data using provided default templates, or upload your own data [CSV/JSONL]... You can then review the model responses and provide a thumbs up or thumbs down rating."

Source: Learn.microsoft.com - Evaluate generative AI apps

Microsoft Learn: View and manage evaluation results

Context: "After you complete a manual evaluation, you can save the results. These results are stored in your project and can be reviewed later in the Evaluation page."

Source: Learn.microsoft.com - See evaluation results in Microsoft Foundry portal

Azure AI Foundry Documentation: Manual Evaluation

Context: "Manual evaluation... allows you to import a test dataset... manually review the application's generated outputs... [and] provide feedback (thumbs up/down)."

Source: Learn.microsoft.com - Concepts - Evaluation