Dataset management

Teams build datasets from production LLM traces by using an observability platform to log every input/output pair during live usage. Teams can then use metadata and search functions to filter this logged data (e.g., filtering for high-cost runs or runs that received a specific user feedback tag) and export the resulting subset directly into a versioned evaluation dataset for testing, debugging, and continuous improvement.

Teams label LLM outputs at scale by combining pairing automated judges with human-in-the-loop (HITL) review. In practice, this means running prompts against large datasets, using LLM-based judges for initial labeling, and applying human-in-the-loop workflows to validate or correct results. This approach balances speed with accuracy and produces reliable labels for evaluation and improvement.

Eval datasets lose reliability as prompts evolve and user behavior shifts, leading to subtle prompt decay. Teams address this by regularly refreshing datasets with recent production LLM traces to capture new edge cases and shifting user behavior. The frequency depends on the pace of application updates and user traffic, but a recurring evaluation template should be run every time a new prompt version is considered.

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The main risk of synthetic data in LLM evaluation is false confidence. Synthetic examples often fail to capture real user behavior and production edge cases, which can cause teams to over-optimize for hypothetical scenarios, resulting in a model that performs well on tests but poorly in a live environment.