Measuring the performance of large language models (LLMs) is critical before deploying generative AI applications to production. Azure AI Foundry offers a comprehensive evaluation framework to assess model quality, safety, and reliability. This guide walks through the key steps:
Setting Up Evaluations Begin by configuring your environment in Azure AI Foundry. Ensure you have the necessary permissions and resources to run evaluation workflows.
Key Metrics Focus on four core dimensions:
- Groundedness: How well the model's responses are supported by provided context.
- Relevance: Whether the output addresses the user's query.
- Coherence: Logical flow and consistency of the response.
- Fluency: Natural language quality and readability.
Safety & Risk Assessment Test for vulnerabilities such as jailbreaks, toxic content, and protected material. Azure AI Foundry includes built-in detectors for these risks.
Automated vs. Manual Evaluation Leverage automated evaluators for speed and consistency, but incorporate human-in-the-loop reviews for nuanced assessment. The platform supports both approaches.
Interpreting Results Use the evaluation dashboards to analyze performance data, identify weak spots, and refine your prompts or model configurations. Iterate based on insights to improve your GenAI application.
Whether you're a data scientist or AI engineer, this tutorial ensures your models are both high-performing and safe for end users.