Braintrust Autoevals: CI Gates for LLM Regressions
Braintrust has introduced Autoevals, a library designed for evaluating LLM outputs to ensure quality and catch regressions. This tool is part of a broader evaluation platform that aims to improve the reliability of AI applications. By implementing various checks, Braintrust seeks to enhance the continuous integration process for LLMs, addressing the unique challenges posed by their output variability.
- ▪Braintrust's Autoevals library is focused on scoring model outputs for LLM applications.
- ▪The evaluation process includes checks for JSON validity, schema compliance, and semantic accuracy.
- ▪Braintrust aims to provide a systematic approach to measuring AI application quality and catching regressions before production.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1909290) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Jangwook Kim Posted on May 20 • Originally published at effloow.com Braintrust Autoevals: CI Gates for LLM Regressions #braintrust #autoevals #llmevals #ci LLM applications need a different kind of regression test. Unit tests can tell you whether a function returns a value, but they do not tell you whether an assistant quietly changed a refund action, dropped a required field, or returned valid JSON with the wrong business meaning.
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