Generating synthetic data is a significant step, but its utility hinges on its quality and how well it integrates into your LLM workflows. This chapter addresses the essential phase of evaluation and troubleshooting. You will learn to systematically assess the synthetic data you produce and navigate common operational challenges.

We will cover:

• Methods for quantitative analysis using specific metrics, for example, perplexity denoted as $PPL$, or diversity scores like $D_s$.
• Techniques for qualitative review to ensure content coherence and relevance.
• Strategies for identifying and mitigating bias within your generated datasets.
• Approaches to manage factual integrity and reduce the occurrence of model-generated inaccuracies.
• Understanding the causes of model performance degradation, sometimes termed model collapse, and how to counteract it.
• Techniques for ensuring the originality and variety of your synthetic data.
• Developing a practical validation checklist to guide your assessment process.

By the end of this chapter, you will be equipped to not only generate synthetic data but also to critically evaluate its fitness for purpose and address potential issues that arise in its application.