Show HN: Dbt-LLM-evals – Monitor LLM quality in your data warehouse (github.com)

The newly announced dbt-LLM-evals package enables users to evaluate the outputs of Large Language Models (LLMs) directly within their existing data warehouses, eliminating the need for external API calls and data egress. This tool is pivotal for AI/ML practitioners as it automates the quality assurance of AI-generated content using the "LLM-as-a-Judge" method, where one LLM assesses another’s outputs against established baselines. By systematically scoring outputs on various criteria such as accuracy and relevance, it not only facilitates performance monitoring but also aids in drift detection, ensuring that production models consistently meet business standards. Significantly, dbt-LLM-evals integrates seamlessly with major data warehouses like Snowflake, BigQuery, and Databricks, utilizing native AI functions for evaluations. Its features include automated baseline detection, flexible sampling, and comprehensive reporting capabilities, all designed to enhance model reliability without incurring high costs. With built-in functionality for capturing prompts and outputs, this package provides actionable feedback to improve AI systems over time. By enabling teams to monitor model performance continuously and transparently, dbt-LLM-evals positions organizations to maintain high levels of confidence in their AI deployments.