2_use_case_name: Lex Natural Language Interface 
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: Create an efficient and user-friendly system that enables users to query project data, such as funding, AUs (allocation units), project focus, and fiscal years, with natural language prompts.

12_outputs: The system executes a query against a postgres database and displays an LLM-generated textual summary of returned query records.  The system also displays the LLM generated queries. 

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/25/2024
19_date_acq_dev_began: 10/1/2024
20_date_implemented: 11/1/2024
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: Yes
33_agency_data: Structured data from a PostgreSQL database containing information about HPC (High-Performance Computing) projects.

34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case's development.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
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: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
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: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: Misinterpretation of data due to model errors could lead to missed opportunities for users in receiving accurate project information. Inaccurate query results or inefficient use of resources may lead to financial costs associated with misinformed decision-making.

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: Online - Site will have user terms of service persuant to Section 5(c)(iv)(I) of OMB Memorandum M-24-10.

61_adverse_impact: No
62_disparity_mitigation: Process to detect bias and fairness are in testing and being regulary evaluated by model provideers. There are built-in guardrails in place to prevent abuse

63_stakeholder_consult: None of the above
65_appeal_process: Yes
67_opt_out: Yes