2_use_case_name: ChatCDC- CDC Enterprise GenAI Chatbot: Bring your own Enterprise Data (Data Analysis)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI system is an extension of the GenAI chatbot but is only available to a small group of pilot testers as of November 5, 2024. It operates through an ATO'ed instance of Microsoft Azure OpenAI through CDC's Enterprise Data Analytics and Visualization Platform. Using a method called retrieval augmented generation (RAG), this enhancement enables staff to augment the existing GenAI chatbot with access and “knowledge” of internal or enterprise data such as documents, spreadsheets, and PDFs. Additionally, each chatbot response has citations referencing to the sections of source text that informed the response, which facilitates human-in-the-loop validation for accuracy and relevancy of AI-generated content.

One way users are using this tool is for data extraction, which accelerates analysis of large document sets. By extracting key points from large volumes of text in various file formats, the enterprise-data-enhanced chatbot streamlines access to critical insights from reports, internal documentation, publications, and other documents. For example, one team is using the chatbot to support literature review by extracting information related to housing status and HIV in the US from a set of pre-screened publications. Examples of this information include a binary yes/no on whether the research paper focuses on the topic of interest, the paper’s population of focus, and the type of paper.

The enterprise data chatbot’s ability to quickly and accurately extract relevant information reduces manual search time and errors associated with human data retrieval. The output will be leveraged for more accurate data analysis, timely insights, and better-informed decision-making processes.
12_outputs: When the user enters a prompt or query related to data extraction into the chatbot, the tool uses a GPT-4 model and Azure AI Search within a RAG architecture to retrieve relevant sections of text from the provided enterprise data sources and extract key information as instructed by the user’s prompt.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 6/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.