Case Study: Databricks achieves faster, more cost-effective LLM evaluation with SuperAnnotate

Databricks optimizes model evaluation with SuperAnnotate's expertise

Databricks, a leading data and AI company, needed a reliable and data-driven method to evaluate the foundational models for its RAG systems. The subjective nature of large language model responses and the difficulty in collecting feedback at scale posed significant challenges, so they partnered with SuperAnnotate.

SuperAnnotate provided its highly customizable data annotation platform and expertise to create a standardized grading rubric and purpose-built tooling for evaluation. This partnership enabled Databricks to benchmark its models and refine its prompts. As a result, Databricks achieved a tenfold reduction in evaluation costs and a threefold increase in speed by using a more cost-effective model.

Databricks

Quinn

Senior Software Engineer