Office of Advocacy AI System-Prompt Suite
Description
Federal staff spend significant time on repetitive text-processing tasks such as rewriting, summarizing, drafting replies, checking grammar, and fact-checking. Traditional approaches require manual effort and lack consistency across analytic workflows. Unstructured AI outputs create ambiguity and are difficult to integrate into automated processes.
Detailed example
Suite of structured Large Language Model (LLM) system prompts for federal text analysis tasks. Provides automated AI capabilities including calendar event generation, text rewriting, reply drafting, text summarization, grammar/spelling checking, and fact-checking. Each prompt demands structured JSON output, transforming LLMs from conversation generators into predictable, machine-usable workflow components. Inputs: unstructured text via API (sentences, documents, emails). Outputs: structured JSON-formatted results (rewrites, summaries, replies, error reports, fact-checks).
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
Significantly increases frequency and quality of analytic work by reducing friction in text processing, improving consistency through structured JSON outputs, enabling automation at scale, making outputs audit-ready and transparent, and supporting reuse across government agencies. Published prompts advance Executive Order 14179 and OMB M-25-21 goals for responsible AI adoption and cross-government innovation.
Audit / financial statement impact
Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
Controls / human review
ATO: Yes; PIA: Not publicly available
Data needed
Commercial LLM provider training data (OpenAI or similar)