Local GenAI Pilot
Description
Local Large Language Models (LLMs) will help in local no software/hardware (SW/HW) cost. LLMs will position IRS for sustainable AI services and AI tool integrations and mitigate many risks [Several Risks Mitigated Include: vendor lock-in, licenses shortages or over purchasing, SW costs or per Application Programming Interface (API) call costs, contract gaps and access loss with outsourcing, and data privacy policies and interagency agreements restricting data leaving the IRS (Auditing/Compliance Algorithm's Source Code)]
Detailed example
Contextually grounded responses derived from the given input prompt. Examples: Text Generation, Summarization, Code Completion, Unit Tests, Text Translation, Text-to-Text Transformation.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Reducing human workload by leveraging LLMs increasing development speed, automation, and operational efficiency. Ensuring alignment with internal Information Technology (IT) policies and security protocols by operating entirely within IRS infrastructure. Eliminates licensing costs reducing operational expenses. Maintains full control over sensitive data, enhancing data governance and compliance with federal privacy mandates. Reducing maintenance costs and technical debt due to legacy code and technology. Aging/departing workforce and lack of expertise with legacy systems increases risk of system failures and operational bottlenecks. Resource optimization, efficiency, and sustainable solutions required to modernize critical IRS operations and services
Audit / financial statement impact
The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
Controls / human review
ATO: Yes; PIA: Not published
Data needed
We have LLMs that are fully pre-trained. Each model is specifically trained on various use case categories like code translation, text summarization etc. We do not perform additional training for these models at this time.