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articles/ai-studio/concepts/evaluation-approach-gen-ai.md

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@@ -54,7 +54,7 @@ Pre-production evaluation involves:
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The pre-production stage acts as a final quality check, reducing the risk of deploying an AI application that does not meet the desired performance or safety standards.
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- Bring your own data: You can evaluate your AI applications in pre-production using your own evaluation data with Azure AI Foundry or [Azure AI Evaluation SDK’s](../how-to/develop/evaluate-sdk.md) supported evaluators, including [generation quality, safety,](./evaluation-metrics-built-in) or [custom evaluators](../how-to/develop/evaluate-sdk.md#custom-evaluators), and [view results via the Azure AI Foundry portal](../how-to/evaluate-results.md).
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- Bring your own data: You can evaluate your AI applications in pre-production using your own evaluation data with Azure AI Foundry or [Azure AI Evaluation SDK’s](../how-to/develop/evaluate-sdk.md) supported evaluators, including [generation quality, safety,](./evaluation-metrics-built-in) or [custom evaluators](../how-to/develop/evaluate-sdk.md#custom-evaluators.md), and [view results via the Azure AI Foundry portal](../how-to/evaluate-results.md).
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- Simulators: If you don’t have evaluation data (test data), Azure AI [Evaluation SDK’s simulators](..//how-to/develop/simulator-interaction-data.md) can help by generating topic-related or adversarial queries. These simulators test the model’s response to situation-appropriate or attack-like queries (edge cases).
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- The [adversarial simulator](../how-to/develop/simulator-interaction-data.md#generate-adversarial-simulations-for-safety-evaluation) injects queries that mimic potential security threats or attempt jailbreaks, helping identify limitations and preparing the model for unexpected conditions.
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- [Context-appropriate simulators](../how-to/develop/simulator-interaction-data.md#generate-synthetic-data-and-simulate-non-adversarial-tasks) generate typical, relevant conversations you’d expect from users to test quality of responses.

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