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Show HN: Nomadic – Minimize RAG Hallucinations with 1 Hyperparameter Experiment

Sep 11, 2024 - news.bensbites.com
NomadicML has launched Nomadic, a platform designed to optimize AI systems through parameter search. The platform aims to improve the performance of AI systems by offering the best-performing, statistically significant configurations for the user's RAG, potentially improving hallucination metrics by 4X in just five minutes. The lightweight library is available on PyPI and allows users to input their model, define an evaluation metric, specify the dataset, and choose which parameters to test.

The team behind NomadicML, who have experience in building Lyft’s driver earnings platform, automating Snowflake’s just-in-time compute resource allocation, and developing a fintech fraud screening system, created Nomadic out of frustration with existing hyperparameter optimization solutions. The goal is to make the process of fine-tuning AI systems more systematic, quick, and interpretable. The platform is still being actively developed, with plans to support text-to-SQL pipelines and a Workspace UI.

Key takeaways:

  • NomadicML has launched Nomadic, a platform focused on parameter search to optimize AI systems.
  • The platform aims to improve the process of setting hyperparameters, which can significantly impact the performance of AI systems.
  • Nomadic is designed to make the process of fine-tuning AI systems more systematic, quick, and interpretable.
  • The team behind Nomadic has a strong background in optimization, having worked on projects such as Lyft’s driver earnings platform and Snowflake’s compute resource allocation.
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