Show HN: Statgate, statistically calibrated ship/block CI gates for LLM evals (github.com)

Statgate is a new tool designed to enhance evaluation processes for large language models (LLMs) by introducing statistically calibrated continuous integration (CI) gates. Traditional evaluation methods can produce misleading results, where small changes in score might be attributed to sampling noise rather than genuine regressions. Statgate addresses this by providing a statistics layer that categorizes evaluation results into "SHIP," "BLOCK," or "INCONCLUSIVE," depending on the statistical significance of the results. This categorization helps teams make informed decisions about code merges rather than relying on potentially erroneous indicators. The significance of Statgate lies in its ability to reduce noise in LLM evaluation, which is critical for maintaining high software quality through accurate regression testing. By leveraging techniques such as bootstrap confidence intervals and sequential stopping rules, it offers a more reliable framework for assessing model performance. Additionally, Statgate operates independently of any LLM calls, relying solely on Python libraries like NumPy and Pydantic, making it accessible and easy to integrate with various evaluation runners. The tool not only aids in determining when to proceed with code changes but also helps quantify the necessary evaluation suite size, ultimately saving both time and resources in evaluating LLM performance.