The RAGTag SNAPpers at Policy2Code Demo Day at BenCon 2024
The team examined how AI, specifically LLMs, could streamline the case review process for SNAP applications to alleviate the burden on case workers while potentially improving accuracy.
Case workers are overwhelmed with the volume of cases that they have to support, yet are held to high standards by the SNAP Quality Control (QC) process. The team evaluated where AI could potentially help streamline the case review process to reduce the burden on case workers while potentially increasing accuracy. For RaC, they assessed the potential for LLMs to review case applications to calculate an initial benefit amount. In their experiment, they built a RAG model using gpt-4o mini with text from Code of Federal Regulations SNAP, then used a model to calculate benefits for 259 applications using SNAP QC 2022 data for the District of Columbia only. They programmatically converted data to text for use in the model.
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