Enhancing the RCDC with Generative AI
Description
The goal is to assess whether generative AI can enhance the RCDC process by minimizing time-consuming, resource-intensive routine and manual tasks and improving overall efficiency.
Detailed example
The Azure cloud platform provides secure access to OpenAI’s ChatGPT model, which is connected to search indexes pre-loaded with relevant datasets. These datasets include internal agency-provided data, such as meeting transcripts, and publicly available data from NIH RePORTER. ChatGPT responds to prompts to execute various tasks such as summarizing meeting transcripts and notes, and the scientific content within curated sets of grant applications. ChatGPT is also utilized to recommend an appropriate RCDC category for a grant application and provide explanations for its recommendations. Additionally, ChatGPT is prompted to predict semantic types for thesaurus concepts, identify hierarchical relationships between concepts, cluster similar concepts, and suggest synonyms for specified terms.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Gen AI is expected to save time, reduce manual workload, and improve productivity by automating resource-intensive processes. These efficiencies support the agency's mission by enabling better resource allocation and enhanced categorization of NIH research.
Controls / human review
ATO: No; PIA: Not published
Data needed
Text data from meeting transcripts and notes, and publicly available grant data from NIH RePORTER database. The data is indexed using Azure Cognitive Search AI and securely stored in an Azure cloud storage container. A Retrieval-Augmented Generation (RAG) chatbot is employed to retrieve the indexed data, and prompts are developed to effectively query the data and generate responses using ChatGPT. LLMs respond in a manner that can cite specific language in the data sources, allowing subject matter experts to validate LLM-generated outputs.