2_use_case_name: LLM EV 
3_agency: Department of Energy
3_abr: DOE
4_bureau: NREL - National Renewable Energy Laboratory (EE)
8_topic_area: Mission-Enabling (internal agency support) 
10_commercial_ai: Collaborating in real-time using AI-assisted tools in word processors.
11_purpose_benefits: Making NREL EV-specific data more accessible for researchers and accelerating research in this field. 

12_outputs: Outputs analysis results from these studies: https://www.sciencedirect.com/science/article/pii/S2666546824000971 

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/21/2024
19_date_acq_dev_began: 7/1/2024
20_date_implemented: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups.
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/NREL/elm/tree/main/examples/ordinance_gpt
40_has_ato: Yes
41_system_name: NREL Stratus AWS Compute Environment, NREL Azure System
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 published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Planned or in-progress.
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: Misrepresentation of local legal policies of EV permitting, in open access datasets. 
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: Online – in the terms or instruction for the service
59_question_type: Including legal disclaimers with associated outputs. 

61_adverse_impact: No
62_disparity_mitigation: Initial spacial validation

63_stakeholder_consult: None of the above
65_appeal_process: Yes
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.