by paradime-io · AI 工具 · ★ 20
Warehouse-native LLM evaluation and monitoring for dbt™ projects Quick Start Guide » Join the Discussion » ⭐️ Star the repo if this helps your LLM monitoring! A complete dbt™ package for evaluating LLM outputs directly within your data warehouse using warehouse-native AI functions. No external API calls, no data egress - everything runs inside your existing data infrastructure. What are LLM Evaluations? LLM evaluations (or "evals") are systematic methods for measuring the quality, accuracy, and performance of Large Language Model outputs.
| Stars | 20 |
| Forks | 3 |
| Language | Python |
| Category | AI 工具 |
| Quality Score | 38.25/100 |
| Open Issues | 8 |
| Last Updated | 2026-02-10 |
| Created | 2026-01-19 |
| Platforms | gemini, python |
| Est. Tokens | ~13k |
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dbt-llm-evals is The warehouse-native LLM evaluation package for dbt™ - monitor AI quality without data egress. It is categorized as a AI 工具 with 20 GitHub stars.
dbt-llm-evals is primarily written in Python. It covers topics such as ai, anthropic, bigquery.
You can find installation instructions and usage details in the dbt-llm-evals GitHub repository at github.com/paradime-io/dbt-llm-evals. The project has 20 stars and 3 forks, indicating an active community.