Lessons Learned from AskGDR: Usage and Impact Analysis of the Geothermal Data Repository's AI Research Assistant: Preprint
- National Renewable Energy Lab., Golden, CO (United States)
In October of 2024, the Department of Energy's (DOE) Geothermal Data Repository (GDR) team officially launched AskGDR, an AI research assistant resulting from the integration of a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets. AskGDR allows GDR users to ask deeper questions about the origin of datasets, the methods used to collect them, and the findings they help support. Using Retrieval Augmented Generation (RAG), AskGDR can be used to summarize findings spread across dozens of papers and technical reports or to extract relevant information describing a single data field. However, generative AI is experimental. The National Renewable Energy Laboratory (NREL) has been collecting metrics on AskGDR and documenting lessons learned during its deployment. This paper will outline the efficacy and impact of AskGDR through analysis of its use, operating costs, number and types of questions asked, and the quality of answers provided.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 3011807
- Report Number(s):
- NREL/CP-6A20-92858
- Resource Relation:
- Presented at 50th Workshop on Geothermal Reservoir Engineering, 10-12 February 2025, Stanford, California
- Country of Publication:
- United States
- Language:
- English
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