Top 10 AI Prompts and Use Cases and in the Government Industry in College Station

As Texas state law and national trends make clear, AI is no longer hypothetical for municipalities - 2025 saw every U.S. jurisdiction introduce AI bills and Texas enacted measures explicitly governing government use and oversight (NCSL 2025 state AI legislation summary), while HB 2818 would create a dedicated AI Division inside the Texas Department of Information Resources to accelerate generative-AI modernization and require project cost‑savings reporting (HB 2818 details at Texas Policy Research), including Legislative Budget Board projections of roughly $8.1M biennial startup costs - so College Station must pair pilots with clear ROI metrics.

City leaders should prioritize transparent inventories, human‑in‑the‑loop safeguards, and measurable KPIs (time saved, error rates, citizen-response time) to prove value; a practical guide to those KPIs is available for municipal planners to adopt today (municipal AI KPI metrics City Hall should track).

Bill	Focus	Note
H 149	AI regulation; government use	Enacted
HB 2818	DIR AI Division; procurement & reporting	LBB projects ~$8.1M biennium
S 1964	AI systems regulation; notification/impact assessment	Enacted

Selection prioritized practicality under Texas law: use cases were scored for legal fit, operational impact, and measurability - favoring pilots that meet TRAIGA's disclosure/biometric limits and cure-window enforcement (including the Attorney General's 60‑day cure period and civil penalties) while delivering clear KPIs for time or cost saved.

Legal-fit criteria drew directly from the Texas Department of Information Resources' tracking of AI bills and the Artificial Intelligence Advisory Council's inventory and ethics mandates, so every candidate must support agency inventories and staff training requirements (Texas DIR AI legislation and guidance).

Risk and design standards required alignment with Practical Law/Thomson Reuters' summary of mandatory AI ethics, impact assessments, and NIST AI RMF consistency to qualify for “heightened‑scrutiny” use cases (Practical Law AI ethics and NIST alignment summary).

Final selection also emphasized citizen-facing transparency and measurable ROI; each top use case includes an implementation checklist to prove impact and a disclosure plan per TRAIGA guidance (TRAIGA compliance and disclosure guidance (Dickinson Wright)).

Selection Criterion	Primary Source
Disclosure & biometric limits	TRAIGA / Dickinson Wright
Inventory, ethics, training	Texas DIR AI Advisory Council
NIST-aligned risk standards & impact assessments	Practical Law (Thomson Reuters)

“At a time when the use of AI is rapidly evolving, it's imperative that we understand how this technology can benefit the public sector. However, we must also ensure we take necessary precautions and implement guardrails to leverage this technology responsibly,” - Amanda Crawford

Citizen-facing chatbots can make front-line services faster and less costly by triaging inquiries and routing residents to the right team - exactly the role the Texas Workforce Commission's “Larry” played when it handled call screening and helped manage the pandemic surge in unemployment cases (Texas Workforce Commission Larry chatbot case study on Nutanix).

For College Station, a Larry‑style pilot should pair generative‑AI conversational models (which can produce human‑like text and speed development) with clear human‑in‑the‑loop handoffs, data‑integrity checks, and KPIs that prove impact on hold times and misrouted calls; a municipal KPI playbook for measuring savings and ROI can guide those metrics (Municipal AI KPI metrics City Hall should track).

The practical payoff: faster citizen resolution and fewer escalations during service spikes.

College Station can leverage the Texas Workforce Commission's job‑matching ecosystem to deliver personalized, measurable employment help: WorkInTexas' powerful matching engine (which lists over 751,000 current openings statewide) and its Virtual Recruiter automate alerts and resume matching so residents get relevant leads instead of generic job lists, while the MyTXCareer mobile app surfaces personalized occupation matches and local training options tied to TWC supports.

For unemployment claimants, timely registration matters - TWC requires WorkInTexas registration within three business days of applying for benefits and documents job‑search activity on a verifiable log - so an AI‑assisted local pilot should combine automated job alerts, coachable résumé templates, and logging helpers that export entries compatible with TWC's work‑search requirements to cut applicant time and reduce missed benefit requests.

Pairing these tools with Metrix Learning's 5,000+ free online courses and in‑office career coaching creates a clear “so what”: faster matches, verifiable work‑search records, and direct pathways from skills to openings.

WorkInTexas statewide job matching platform and Virtual Recruiter and the MyTXCareer mobile app for personalized occupation matches are the operational anchors for this pilot.

Tool	Primary Use	Note
WorkInTexas	Statewide job matching & Virtual Recruiter	~751,023 job openings (statewide)
MyTXCareer	Personalized occupation matches, mobile app	Account saves career pathway & training contacts
Metrix Learning	Free online upskilling	5,000+ courses available via TWC

Automated call transcription turns routine 311 and permit-line calls into searchable, time‑stamped text that improves routing, accountability, and supervisor oversight: a UCaaS contact‑center can combine real‑time speech‑to-text with automatic call routing, voicemail‑to‑email, agent performance dashboards, and role‑based controls so supervisors spot volume spikes and misroutes without replaying hours of audio (UCaaS contact center solutions for Texas municipalities - contact center tools, call analytics & voicemail-to-email).

For College Station, pair transcripts with clear human‑in‑the‑loop escalation rules and KPI reporting (average hold time, transcription accuracy, percent of correctly routed calls) so pilots prove value against measurable targets; a municipal KPI playbook helps set those benchmarks and demonstrate cost‑savings to council (Municipal AI KPI metrics for City Hall - KPI playbook for measuring AI value).

Built‑in encryption and compliance protocols in UCaaS protect citizen data while creating an auditable transcript trail for performance and compliance reviews.

Multilingual emergency translation lets College Station deliver the right instruction to the right resident at the exact moment it matters: QR codes or live captions can put translated press‑briefings and meeting audio directly on a smartphone so non‑English speakers receive instructions simultaneously with English‑language audiences, avoiding hours‑long delays that cost lives and trust; Los Angeles County used an AI captioning tool to stream translations in more than 60 languages during wildfire briefings, while Texas 911 pilots have deployed live call translation to cut interpreter delays in rural jurisdictions (Route Fifty: AI translation in public safety - analysis of translation tools for emergency response, County Magazine: Rio Grande Area Council 911 translation pilot case study); local adoption should pair tested glossaries, human reviewers, and clear KPIs (time-to-understanding, translation accuracy, and percent of residents reached) so City Hall can measure lives‑saved and minutes‑saved in after‑action reviews (News-Journal: QR-enabled real-time language access bridging the language gap).

Tool	Primary Use	Note
Wordly (AI captioning)	Live press‑conference translation	Supports 60+ languages; QR opt‑in for attendees
Carbyne (911 integration)	Real‑time emergency call translation	Deployed in five rural Texas counties to reduce interpreter delay

“Minutes and seconds matter, especially in our rural jurisdiction... the caller understands what is going on the whole time.” - Marisa Quintanilla, Regional Services Director

A practical Fraud Detection Suite for College Station would combine automated anomaly‑scoring, cross‑matches against incarceration and payroll records, insider‑access monitoring, and periodic bias checks so investigators focus on high‑value cases instead of chasing noise - Texas OIG quarterly results show the impact of targeted investigations (4,375 investigations completed and $5,604,952 in fraudulent benefits recovered in Q2 FY2022), while recent HHSC incidents that exposed 61,104 account holders and multiple employee theft cases underscore insider risk and the need for strict access controls and alerts (Texas OIG benefits recoveries and case summaries, Texas HHSC data breach and insider theft report).

Operationalize the suite with ID‑proofing, automated data‑matches (like the TDCJ incarceration checks that triggered an ADH), and vendor‑provided analytics that produce audit logs and explainable risk scores; pair those tools with periodic statistical reviews required by federal UIPL guidance so eligible claimants aren't wrongly blocked (benefits fraud detection system studies and UIPL compliance resources).

The payoff: faster recoveries, fewer wrongful denials, and a tamper‑evident audit trail for prosecutors and council reports.

Metric	Value
Investigations completed (Q2 FY2022)	4,375
Fraudulent benefits recovered (Q2 FY2022)	$5,604,952
HHSC insider breach affected	61,104 account holders

"When a state notices that individuals are unable to verify their ID through web-based or selfservice means, or the state flags individuals as suspicious with automated cross-matching or data analytics activities, states must generate and retain documentation of these instances."

ProcureTrack-style automation ties purchase orders, budgets, invoices and contract records into a single, auditable Procure‑to‑Pay workflow so College Station can speed approvals, reduce manual invoice chasing, and produce the city council-ready reports auditors want; DIR's Software Products catalog explicitly lists “Procurement and Contract Management” and “Procure‑to‑Pay suites” as available offerings, making DIR cooperative contracts a practical place to source tested SaaS solutions (DIR Software Products and Related Services catalog).

shop DIR first

Before buying, verify customer eligibility and procurement rules: the Texas DIR procurement guide requires state agencies to shop DIR first for IT commodities and spells out vendor‑solicitation thresholds that shape how a ProcureTrack pilot should be scoped (for example, purchases over $50,000 but not exceeding $1 million require pricing from at least three DIR vendors), so automation both enforces compliance and creates the transaction trail that proves savings and reduces audit risk (Procurement Professional's Guide to DIR - procurement rules and guidance).

Contract Value	# of Vendors Required (State Agencies)
Up to $50,000	Direct award to DIR vendor(s)
Over $50,000 to $1,000,000	At least 3 DIR vendors/resellers
Over $1,000,000 to $5,000,000	At least 6 DIR vendors/resellers
Over $5,000,000 to $10,000,000	Option to use DIR; if used, solicit at least 6 vendors

BlueShield IDS for College Station should combine AI‑aware detection, rapid playbooked response, and auditable controls so municipal networks stop threats before they escalate into citizen‑facing outages or fraud: implement CISA's AI data‑security practices (data provenance, integrity checks, encryption, and continuous risk assessments) alongside behavioral analytics that flag credential‑stuffing patterns - a threat that drives roughly 16.5% of login‑page traffic and feeds off an estimated 15 billion stolen credentials - so compromised logins don't become fraudulent payouts or service‑disrupting breaches (CISA AI data security guidance and best practices, Credential stuffing detection, examples, and impact analysis by A10 Networks).

Operational rules must also mirror Texas institutional guidance: only deploy platforms with contractual protections and technical controls, keep human review for high‑risk automated actions, and retain tamper‑evident audit logs so council and auditors can trace decisions under System Regulation 29.01.05 (Texas A&M System AI cybersecurity guidance and deployment controls).

The practical payoff: fewer account takeovers, clearer evidence for incident response, and faster return of service to residents when seconds matter.

Component	Purpose	Primary Source
Data provenance & integrity checks	Prevent poisoned or tampered telemetry feeding detection models	CISA AI data security guidance and best practices
Behavioral analytics & device fingerprinting	Detect credential stuffing and botnets despite IP rotation	Credential stuffing detection, examples, and impact analysis by A10 Networks
Contractual + technical controls	Ensure vendor accountability and protect sensitive data	Texas A&M System AI cybersecurity guidance and deployment controls

PolicyBrief AI turns technical complexity into council‑ready guidance by boiling models, risks, and tradeoffs into short, actionable recommendations: define the policy scope; forbid entry of confidential or public‑records‑sensitive data into public tools; mandate human fact‑checking and logging of AI use; require transparency about recordings and transcriptions; and build stakeholder review, plain‑language disclosure, and training into any pilot.

Use the UNC School of Government checklist for concrete guardrails and evidence‑based limits - see the UNC SOG Developing Guidelines for the Use of Generative AI in Local Government - and NACo's AI County Compass toolkit for local governance and implementation to triage low‑risk versus high‑risk implementations.

The practical payoff: a one‑page brief that tells College Station council exactly when to approve a pilot, require human‑in‑the‑loop controls, or pause - avoiding costly public‑records or privacy missteps while preserving measurable KPIs for time, cost, and service quality.

UNC SOG: Developing Guidelines for the Use of Generative AI in Local Government and NACo AI County Compass: Comprehensive Toolkit for Local Governance and AI Implementation

“AI outputs shall not be assumed to be truthful, credible, or accurate.”

CaseFlow Assistant speeds social‑services and parole casework by auto‑generating auditable, human‑reviewable case summaries, surfacing relevant statutes and prior decisions, and flagging inconsistent data for clerk attention - so caseworkers spend less time assembling records and more time exercising professional judgment, while parole panels get consistent, plain‑language summaries that simplify appeals and oversight.

Design should follow Berkman Klein's ethics and governance priorities - narrowing the AI knowledge gap, embedding human‑in‑the‑loop checks, and preventing biased outcomes - and include municipal KPI tracking (time saved per case, error rates, citizen‑response time) drawn from practical City Hall metrics guidance to prove ROI and accountability (Berkman Klein ethics and governance of AI guidance, municipal AI KPI metrics City Hall should track for government efficiency).

For safe pilots, pair the assistant with the city's responsible‑use checklist and disclosure plan so College Station can measure impact, limit risk, and present a single‑page evidence trail to council (complete guide to using AI in College Station government).

Metric	Value
Our Work	177
Community	481
Projects, Programs, and Tools	15
People	170

College Station's Red Team Toolkit should turn adversarial testing from a one‑off pen test into a continuous, transparent evaluation workflow that maps directly to federal and industry standards: the Biden Administration's Executive Order now mandates red‑teaming for dual‑use models and ongoing reporting, so local pilots must document tests and remediate findings before escalation (Pillar Security - AI Red Teaming Regulations and Standards).

Practical steps include scheduled adversarial suites (prompt‑injection, data‑poisoning, evasion), independent third‑party evaluations, and human‑in‑the‑loop signoffs tied to NIST AI RMF and MITRE ATLAS guidance so vulnerabilities are reproducible, mitigated, and auditable (Practical DevSecOps - Best AI Security Frameworks for Enterprises).

The payoff is concrete: documented red teams catch subtle failure modes (model inversion, biased scoring, prompt injection) before they become public records headaches or discriminatory decisions, producing a short evidence packet City Hall can present to council and auditors to justify continued deployment.

Framework / Reg	Role in Red Teaming
Executive Order (Oct 2023)	Mandates red‑teaming and reporting for dual‑use models
NIST AI RMF	Lifecycle testing, differential & adversarial testing
MITRE ATLAS	Catalogs adversarial tactics and test cases for red teams

“AI offers a once‑in‑a‑generation opportunity to improve the strength and resilience of U.S. critical infrastructure, and we must seize it while minimizing its potential harms.” - Alejandro N. Mayorkas

College Station's clear next step is to operationalize the guardrails already arriving from Austin: complete a citywide AI inventory aligned with the Texas Artificial Intelligence Advisory Council's expectations, prioritize low‑risk pilots that can qualify for DIR's regulatory sandbox protections, and lock in staff preparedness ahead of mandatory training deadlines so deployments meet TRAIGA's disclosure and cure‑period requirements; legal summaries and the TRAIGA timeline explain both the sandbox option and the Attorney General's enforcement posture (Benesch law firm summary of TRAIGA and regulatory sandbox details) while DIR's council work shows why an agency inventory and ethics oversight should drive pilot scope (Texas Department of Information Resources AI Advisory Council guidance and inventory requirements).

Pair those policy actions with practical staff upskilling - start with a focused cohort in an applied course like Nucamp's AI Essentials for Work so employees learn human‑in‑the‑loop controls, prompt design, and measurable KPIs before broad rollout (Nucamp AI Essentials for Work registration and course details) - so College Station can start small, prove savings to council, and govern well under Texas law.

Immediate Next Step	Deadline / Note	Source
Complete AI inventory	Per DIR reporting requirements (inventory guidance)	Texas DIR AI inventory and advisory council announcement
Plan staff training	Align with HB 3512 mandatory training	Baker Data Counsel analysis of Texas AI policy and HB 3512 training implications
Scope sandbox‑eligible pilots	Consider sandbox protections and reporting rules	Benesch explanation of sandbox eligibility and reporting under TRAIGA

“At a time when the use of AI is rapidly evolving, it's imperative that we understand how this technology can benefit the public sector. However, we must also ensure we take necessary precautions and implement guardrails to leverage this technology responsibly,” - Amanda Crawford