Generating Synthetic Data for Evaluation and Edge-Case Testing
Explore how to generate structured synthetic data to intentionally expose diverse behaviors and edge cases in LLM systems. Learn to define evaluation dimensions, manually create scenario tuples, and leverage large language models to convert these into realistic user inputs. This lesson helps you design meaningful synthetic traces that guide targeted testing and uncover failure points early in development.
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Capturing traces from real users is ideal, but in the early stages, it is often insufficient and sometimes not viable. Many systems do not yet have enough usage, and even when they do, user behavior tends to cluster around a narrow set of common paths. As a result, important edge cases and failure modes may never appear naturally. Synthetic data enables you to intentionally guide the system through a broader range of behaviors, allowing for the collection of more diverse traces for evaluation and analysis.
The goal of synthetic data is not to create fake users, but rather to generate realistic data. The goal is to create inputs that exercise different execution paths within the system. When done well, synthetic inputs help you uncover failures earlier, before they affect real users. When done poorly, they produce generic traces that offer little insight. The difference comes down to structure.
Why does unstructured synthetic data fail?
A common ...