2_use_case_name: ChatCDC- CDC Enterprise GenAI Chatbot: Bring your own Enterprise Data (Summarization)
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.

The enterprise data chatbot’s summaries will enhance operational, informational, and decision-making processes that would otherwise require extensive research and reading time. For an example of a more informational use case, one team provided the chatbot with documentation and resources about a division’s organizational structure, projects, administrative processes, and tools to support the orientation of new staff and (re)orientation of existing staff. The chatbot enables the division’s staff to efficiently find resources for their roles; better understand the division’s initiatives; and track the activities involved in the onboarding process for new staff.
12_outputs: When the user enters a prompt or query related to summarization 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 condense that information into concise summaries in response to 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.