Acrolinx fix

theresa-i · theresa-i · commit b19a529fe581 · 2025-07-11T13:46:33.000-04:00
diff --git a/learn-pr/wwl-data-ai/evaluate-language-models-azure-databricks/includes/1-introduction.md b/learn-pr/wwl-data-ai/evaluate-language-models-azure-databricks/includes/1-introduction.md
@@ -2,4 +2,4 @@ Large Language Models (LLMs) have transformed how we build applications, powerin
 
 Evaluation is essential for successfully deploying LLMs to production. You need to understand how well your model performs, whether it produces reliable outputs, and how it behaves across different scenarios.
 
-In this module, you'll learn to evaluate LLMs by comparing evaluation approaches, understanding how individual model evaluation fits into broader AI system assessment, applying standard metrics like accuracy and perplexity, and implementing LLM-as-a-judge techniques for scalable evaluation.
+In this module, you'll learn to evaluate LLMs by comparing evaluation approaches, and understanding how individual model evaluation fits into broader AI system assessment. You'll also learn about standard metrics like accuracy and perplexity, and implementing LLM-as-a-judge techniques for scalable evaluation.

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`@@ -2,4 +2,4 @@ Large Language Models (LLMs) have transformed how we build applications, powerin`
`2`	`2`
`3`	`3`	`Evaluation is essential for successfully deploying LLMs to production. You need to understand how well your model performs, whether it produces reliable outputs, and how it behaves across different scenarios.`
`4`	`4`
`5`		`-In this module, you'll learn to evaluate LLMs by comparing evaluation approaches, understanding how individual model evaluation fits into broader AI system assessment, applying standard metrics like accuracy and perplexity, and implementing LLM-as-a-judge techniques for scalable evaluation.`
	`5`	`+In this module, you'll learn to evaluate LLMs by comparing evaluation approaches, and understanding how individual model evaluation fits into broader AI system assessment. You'll also learn about standard metrics like accuracy and perplexity, and implementing LLM-as-a-judge techniques for scalable evaluation.`