Top 10 AI Prompts and Use Cases and in the Government Industry in Lawrence

Lawrence city leaders and public works teams must treat AI prompts as practical tools - prompt-driven searches and generative workflows help staff find federal grants, parse RFPs, flag procurement risks, and streamline permitting without replacing human judgment; responsible-adoption guidance urges staff involvement and vendor transparency to avoid harms like workplace stress and bias (Responsible AI adoption guidance for local governments), while implementation playbooks show immediate wins in budgeting, asset maintenance, and permitting when prompts are tuned to municipal data (AI tools for municipal budgeting, procurement, and permitting).

A practical next step: train prompt-writing and operational skills locally - Nucamp's AI Essentials for Work bootcamp teaches prompt craft and applied use cases for municipal teams (Nucamp AI Essentials for Work bootcamp: prompt-writing and municipal use cases), enabling semantic search of public records that surfaces critical documents in seconds so staff can reinvest saved time into frontline services.

Bootcamp	Details
AI Essentials for Work	Length: 15 Weeks; Courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; Early bird cost: $3,582; Register for Nucamp AI Essentials for Work (15-week bootcamp)

“The success or failure of introducing AI in local government requires workers' knowledge and ongoing input regarding use of the new technology and its impacts within municipal departments and upon the public at-large.”

Prompts and use cases were compiled by mining GovTribe's consolidated federal, state, and local procurement data and then stress-testing those prompt patterns against municipal workflows for Lawrence: saved searches and alerts were tuned to place-of-performance and local NAICS/PSC filters so city teams see only relevant solicitations, vendor profiles were used to model teaming and outreach prompts, and captured RFP/award metadata formed the basis for requirement-extraction and evaluation prompts (GovTribe opportunity aggregation for federal, state, and local procurements).

GovTribe's AI Insights and Elastic-backed semantic search informed prompt phrasing, retrieval-augmented generation (RAG) patterns, and chatbot-style Q&A for rapid synthesis (Elastic semantic search and GovTribe AI Insights case study), while Nucamp's Lawrence-focused guides ensured each prompt maps to municipal tasks like grant narratives, procurement screening, and public-records search (Nucamp Web Development Fundamentals - Lawrence government coding resources and semantic search guidance).

The payoff: actionable prompts that reduce noise and deliver precise RFP triggers and vendor matches for Lawrence staff when timing matters most.

Source / Tool	Role in methodology
GovTribe platform for consolidated opportunity aggregation	Aggregated opportunities, filters (NAICS/PSC, set-aside, place-of-performance), saved searches, vendor profiles for watchlists and prompt targets
Elastic semantic search powering GovTribe AI Insights	Semantic search, RAG and chatbot used to generate, test, and refine prompt phrasing and requirement extraction
Nucamp Lawrence resources for local public-records semantic search	Validated municipal workflows and example prompts for semantic search of local public records and operational use cases

“The integration of AI-backed capabilities is no longer optional. It's a fundamental requirement for remaining competitive and offering effective, timely solutions to our customers. Elasticsearch - and its vector database - plays a critical role in this delivery.”

Prompt 1 - example:

Search and return active federal and local public‑works solicitations with place‑of‑performance Lawrence, KS (Douglas County), prioritizing design/construction contract opportunities and flags for bonding or Buy‑American clauses; include solicitation IDs, due dates, procurement portal links, and whether a performance/payment bond (Miller Act) or IRA low‑embodied‑carbon material language appears.

Use this routine to monitor federal feeds (register on SAM.gov and watch Contract Opportunities via the GSA guidance on bidding for construction projects), while also subscribing to Lawrence's eProcurement notifications and vendor portal to capture city-level formal (> $50,000) and informal ($5,000.01–$50,000) solicitations; Lawrence posts bids to OpenGov and requires free vendor registration and electronic bid bonds via Surety2000, with public bid openings on Tuesdays at 3:35 p.m.

Local outreach is completed by creating a bids&tenders account for Douglas County opportunities. Running this prompt hourly cuts days off proposal discovery and ensures teams hit Tuesday openings and bond deadlines.

Item	Key detail
Lawrence vendor portal	Lawrence KS OpenGov procurement - Purchasing Division (vendor registration)
Douglas County portal	Douglas County bids&tenders vendor registration
Federal construction rules	GSA guidance on bidding for federal construction projects (SAM.gov Contract Opportunities, Miller Act, IRA LEC)
Thresholds & timing	Informal: $5,000.01–$50,000 • Formal: > $50,000 • Miller Act bonds: > $100,000 • Bid openings: Tuesdays 3:35 p.m.

Prompt 2 - example:

List open and forecasted federal grant opportunities for municipal infrastructure that accept local governments in Kansas or list Lawrence, KS (Douglas County) as place‑of‑performance; prioritize competitive NOFOs under the Bipartisan Infrastructure Law such as RAISE, PROTECT, NEVI, Bridge Investment, and Safe Streets and Roads for All; for each result return agency, program name, eligibility, estimated award range (or five‑year funding total when available), next milestone/close date, required local match, and the application link.

Start this workflow by running the Funding Pathfinder to get a curated, BIL/IRA‑aligned shortlist and supporting agency resources (Local Infrastructure Hub Funding Pathfinder - curated funding opportunities), cross‑reference the Department of Transportation's IIJA grant catalog and BIL Launchpad for program categories and five‑year totals to prioritize modal and resilience grants (DOT IIJA grant programs and BIL Launchpad guidance), then validate eligibility and submit or set alerts via Grants.gov so applications and NOFO changes are tracked centrally (Grants.gov search and application portal).

So what: a single, repeatable prompt tuned to Kansas place‑of‑performance turns broad federal funding streams into a short, prioritized worklist that helps municipal teams focus limited preparation time on the NOFOs most likely to fit Lawrence's infrastructure schedules and match requirements.

Resource	Use
Funding Pathfinder (Local Infrastructure Hub)	Curated BIL/IRA program matches and agency resources for project leaders
DOT IIJA Grant Programs	Program categories, five‑year funding totals, and BIL Launchpad guidance to prioritize transportation/resilience grants
Grants.gov	Search NOFOs, register applicants, set alerts, and submit federal grant applications

Prompt 3 - example:

“Search GSA's Subcontracting Directory and SBA SUBNet for OTSB prime contractors with active construction contracts that list Kansas or Lawrence, KS as place‑of‑performance; filter by NAICS codes for highway/bridge (e.g., 237310), construction management, and site work, return UEI, prime contract IDs, subcontracting plan status, recent subcontracting reports, SBLO or point‑of‑contact, and links to posted opportunities.”

Use the GSA Subcontracting Directory to identify primes required to have small‑business subcontracting plans and to pull UEIs and NAICS matches, query SUBNet by state/NAICS to see live posted subcontracting opportunities, and consult NAVFAC's subcontracting listings where applicable - each NAVFAC prime contract includes a Small Business Liaison Officer contact for outreach.

Prioritize results showing recent eSRS reports or explicit subcontracting goals; so what: this prompt turns broad federal feeds into a short list of reachable primes and SBLO contacts that Lawrence subcontractors can call before bid cycles to position teams for prime flow‑downs and capture higher‑value subcontracts.

Directory Field	Example / Description
Unique Entity ID (UEI)	U9PKUDH7SPX (example entry)
Vendor name	Spatial Inc. (example entry)
Vendor address	8614 Westwood Center Dr Ste 350, Vienna, VA (example entry)
NAICS code	611400 (directory field - searchable by NAICS)
Major products / service lines	Listed in directory to match subcontracting needs

Prompt 4 - example:

"List named decision‑makers and procurement points of contact at the Kansas Department of Transportation (KDOT) who influence specs, award timelines, and DBE goals for projects in Lawrence, KS; include role, office (e.g., Fiscal Services Procurement - buses, Design/Contracts, Program & Project Management), direct email or phone where published, and any active solicitations or program lines they manage."

Start the search with KDOT's Special Procurement page to capture program‑level procurement officers (for example, Jessica Godfredson is listed as the Procurement Officer for the Kansas Bus Procurement Program, including EV bus solicitations) - see the KDOT Special Procurement bus program page: KDOT Special Procurement - Kansas Bus Procurement Program.

Use the KDOT FAQ contact for design and contract questions (KDOT.DesignContracts@ks.gov) to surface document owners and addenda authors - see KDOT FAQ and Design/Contracts contact: KDOT FAQ / Design & Contracts Contact.

Cross‑check state procurement leadership and procurement officers at the Kansas Department of Administration to identify escalation and statewide procurement policy contacts (names and direct lines help schedule pre‑bid calls) - see the Kansas Department of Administration procurement staff directory: Kansas Department of Administration Procurement Staff Directory.

So what: targeting these three buckets - program procurement officers, design/contracts owners, and state procurement leadership - reduces time to clarify mandatory requirements and bonding/spec questions that commonly delay Lawrence bids and grant‑driven projects.

Contact / Office	Role / Published info
Jessica Godfredson - KDOT Fiscal Services Procurement	Procurement Officer, School & Activity Buses (Kansas Bus Procurement Program) - listed on KDOT Special Procurement
KDOT.DesignContracts@ks.gov	Design/Contracts questions and addenda contact (published on KDOT FAQ page)
Division of Program & Project Management (KDOT)	Eisenhower State Office Building - Phone: (785) 296-2252 (STIP contact)
Kansas Dept. of Administration - procurement staff	Names & phones (example: Candace Smith Deputy Director 785-296-7072; Phil Curtis Procurement Officer III 785-296-2985; Michelle Brown Procurement Officer II 785-296-2401) - staff directory

Prompt 5 - example: instruct an AI to dissect an RFP and return (1) a labeled extraction of mandatory requirements (licenses, bonding, insurance, pass/fail compliance flags), (2) the stated evaluation criteria with suggested weights and a 0–100 scoring matrix, (3) all submission rules (page limits, forms, portal links) and deadlines, and (4) a prioritized list of clarifying questions to submit during the Q&A period; the routine should map each extracted item to the RFP section (scope, objectives, budget, compliance) so reviewers can triage deal‑breakers first and focus writing time on high‑weight criteria like technical approach or past performance.

Use GenAI for initial parsing and table output but validate extracted clauses manually (OCR and human review recommended) so no compliance requirements are missed.

This pattern follows best practices for breaking down RFPs and building transparent scoring rubrics (Dissecting an RFP: Guide for Extracting Key Information), aligns with standard weighted matrix scoring approaches (RFP Evaluation Criteria and Weighting), and can be automated with targeted Generative AI commands while retaining human validation steps (Generative AI Extraction Steps for RFP Requirements).

So what: Lawrence teams get a compliance-first checklist and a scoring template that filters out non‑compliant bids before drafting, reducing rework and the risk of disqualification.

Extracted Item	Why it matters
Mandatory requirements (bonding, licenses)	Pass/fail filter to avoid disqualification
Evaluation criteria & suggested weights	Focuses proposal language on high‑impact elements
Submission rules & deadlines	Ensures format compliance and on‑time delivery
Clarification questions	Reduces ambiguity and prevents scope creep

Prompt 6 - example: ask the AI to produce a submission‑ready draft grant narrative for a Lawrence, KS community resilience project that opens with a concise needs statement tied to local hazard data, then lays out a two‑year workplan (planning → design → pilot implementation) with measurable outcomes, a resident‑led engagement strategy, and a clear budget narrative that maps activities to line items and match sources; cite funder priorities (environment, human services, disaster relief) to align with prospective foundations such as The Lawrence Foundation grant opportunities (The Lawrence Foundation (environment & human services)), use the NEH sample grant application narratives as a structural template for purpose, methods, and evaluation language (NEH sample grant application narratives), and borrow community engagement and two‑year stormwater planning elements from comparable city plans to show feasibility and public buy‑in (Green Lawrence Blue Merrimack Stormwater Resilience Plan, Green Lawrence Blue Merrimack - Stormwater Resilience Plan).

Require the AI to append a brief compliance checklist (environmental review, reporting cadence, labor standards/Davis‑Bacon forms where applicable) and a one‑page logic model with outputs and short‑term outcomes so Lawrence staff can swap local metrics and submit quickly to both private foundations and government NOFOs.

Element	Why include it
Funder alignment	Match project goals to funder priorities (environment, human services, disaster relief)
Narrative structure	Use NEH sample formats for clear purpose, methods, evaluation
Implementation timeline	Two‑year planning/design/pilot example shows feasible milestones and community engagement

“The idea of re-imagining our city, what the city looks like –whether it's the streets or public spaces, or the creation of green spaces – is very exciting,” says task force member Jorge Hernandez.

The International AI Safety Report 2025 offers Lawrence a science‑first roadmap for municipal AI governance: it synthesizes evidence on what general‑purpose AI can do, catalogs three risk families (malicious use, malfunctions, systemic risks), and describes technical mitigations - defense‑in‑depth, continuous monitoring/intervention, and privacy‑preserving methods - that local governments can fold into procurement and oversight language International AI Safety Report 2025 - evidence on advanced AI risks & mitigations.

For Kansas cities this means concrete steps that align with state and national momentum on AI governance: require vendors to document layered monitoring and incident‑response plans, to certify privacy techniques such as differential privacy or confidential computing where PII is involved, and to submit red‑teaming or audit results as part of RFP compliance - measures the Report highlights as necessary for managing evolving threats.

International coordination at the AI Action Summit underscores why municipalities should mirror national risk thresholds and reporting practices so local rules interoperate with state/federal review processes AI Action Paris Summit 2025 - governance takeaways.

So what: a short procurement clause requiring vendor monitoring metrics and a post‑deployment audit can convert the Report's scientific findings into a near‑term protection that prevents service outages, privacy leaks, and costly rework.

Risk (from Report)	Municipal governance focus
Malicious use (deepfakes, cyber)	Require vendor threat‑modeling, red‑teaming, and content provenance/watermarking
Malfunctions (hallucinations, bias)	Mandate monitoring, human‑in‑the‑loop reviews, and bias‑testing reports
Systemic risks (privacy, concentration)	Include privacy‑preserving tech, data‑minimization, and diversity of suppliers

“The report does not make policy recommendations. Instead it summarises the scientific evidence on the safety of general-purpose AI to help ...”

Prompt 8 - example: instruct an AI to ingest solicitations, contracts, invoices, change orders, and vendor records and return ranked fraud‑risk flags (kickbacks/bribery, billing manipulation, change‑order abuse, fictitious vendors, collusive bidding, product substitution, altered or missing supporting docs, and conflicts of interest), tying each flag to the exact clause, invoice line, or vendor field that triggered it so Lawrence auditors can triage high‑risk items quickly; include automated checks for common signals such as invoices repeatedly just beneath approval thresholds, repeated low bids followed by large change orders, vendor addresses that are mail drops or match employee addresses, unexplained increases in payments to a vendor, and altered/soiled invoices or duplicate invoice numbers.

Train the routine on the DoD Inspector General's fraud red‑flags taxonomy to catch Defense‑style schemes and cross‑validate with broader procurement patterns from industry guidance on common schemes and whistleblower triggers (DoD Inspector General fraud red flags and indicators, IACRC procurement fraud schemes and primary red flags); so what: automated screening that surfaces a handful of high‑confidence flags reduces time‑to‑investigation and focuses limited Lawrence audit resources where national data show the greatest losses.

Indicator	Red‑flag example
Billing manipulation	Duplicate or altered invoices; many invoices just under approval thresholds
Change‑order abuse	Low bid award followed by numerous unexplained change orders
Collusive bidding	Identical bids, shared addresses/phones, rotating winners
Fictitious vendor	Vendor not verifiable, mail‑drop address, same address as employee
Product substitution	Cheaper materials used than those specified; high failure/complaint rates

“Government procurement fraud costs federal agencies hundreds of billions of dollars per year. With the Trump administration's focus on fighting fraud, waste, and abuse, this is an area that is likely to receive significant attention in the years ahead.”

Prompt 9 - example: instruct an AI to monitor agency spending feeds for Kansas (place‑of‑performance Lawrence/Douglas County) and surface awards or budget accounts where obligations appear but outlays lag - return award/contract IDs, agency, Treasury account, current obligations vs.

outlays, any reported unobligated balances, last data timestamp, and portal links so staff can rapidly assess eligibility and ask for interagency transfers or pass‑through funding before the federal fiscal year closes on September 30; automate daily checks against USAspending and the Treasury Monthly/Daily Treasury Statement datasets and set alerts on changes or new obligations so late‑cycle opportunities are not missed.

Use CBO budget calendars and projections to prioritize agencies with accelerating year‑end spend. So what: a single, repeatable daily prompt tuned to Kansas place‑of‑performance turns noisy federal feeds into a short list of near‑term, actionable leads - giving Lawrence a practical window of weeks (not months) to secure small, time‑sensitive awards or adjustments that fund immediate municipal needs.

USAspending federal spending database • Treasury Fiscal Data Monthly and Daily Treasury Statements • Congressional Budget Office budget calendar and projections

Source	What to monitor
USAspending federal spending database	Award/contract obligations, place‑of‑performance filters, award IDs and release notes
Treasury Fiscal Data Monthly and Daily Treasury Statements	Monthly outlays and daily cash flows to spot timing gaps between obligations and payments
CBO budget calendar and projections	Fiscal year calendar and agency budget momentum to prioritize targets

Prompt 10 - example: instruct the AI to find teaming partners and vendors similar to a named firm by matching NAICS/PSC codes, recent prime awards, subcontracting‑plan status, and place‑of‑performance filters for Kansas (Lawrence/Douglas County); start by comparing the target's service lines to top primes - for example, Lockheed Martin (defense & aerospace; 2023 defense revenue > $64B) and RTX lead in advanced weapons and systems, L3Harris focuses on communications and electronic warfare, Leidos on IT modernization and cybersecurity, Booz Allen on AI/ML and data analytics, and Amentum on engineering/technical services - then surface nearby primes or regional subs with shared NAICS, UEIs, SBLO contacts, and recent Kansas PO‑listed task orders so Lawrence teams can prioritize outreach before bid cycles (Top 10 US Government Contractors 2024 - contractor profiles & recent contracts).

Tie that output to municipal workflows by running the same prompt against local semantic indexes of public procurement and vendor registrations so the list returns Lawrence‑relevant subcontract leads and contactable SBLOs rather than national-only matches (Semantic search for public records tailored to Kansas agencies).

So what: a single prompt that matches capability, contract history, and Kansas place‑of‑performance turns an overwhelming universe of primes into a phoneable short list - giving small Lawrence firms specific primes and SBLO names to call during pre‑bid windows, which materially increases odds of capture through early teaming conversations.

Company	Primary specialties (source)
Lockheed Martin	Defense & aerospace; advanced military systems (ExecutiveBiz)
Leidos	IT modernization, cybersecurity, enterprise services (ExecutiveBiz / USFunds)
Booz Allen Hamilton	AI/ML, data analytics, digital transformation (ExecutiveBiz)
L3Harris Technologies	Communications systems, electronic warfare, tactical radios (ExecutiveBiz)
Amentum	Engineering, technical services, defense logistics (ExecutiveBiz / USFunds)

Lawrence teams should convert the reportable guidance into an operational checklist: stand up a cross‑department AI oversight group that includes procurement, IT, legal, and frontline staff (short‑term 1–2 years) to adopt tiered governance and public engagement practices from the National Academies' rapid consultation (Strategies for Integrating AI into State and Local Government - National Academies Rapid Consultation), pilot prompt workflows that parse RFPs and monitor SAM.gov/GovTribe feeds so bids and grants are surfaced daily (capture Tuesday 3:35 p.m.

local bid openings and late‑cycle federal obligations), and require vendor controls such as monitoring metrics and red‑teaming/audit results in contracts following international safety guidance.

Pair policy with practical training - schedule city staff for prompt‑writing and operational upskilling (Nucamp's AI Essentials for Work bootcamp covers prompt craft and municipal use cases; AI Essentials for Work bootcamp registration) - and coordinate with existing local efforts like the Lawrence school district's new AI committee to align community engagement and transparency (Lawrence school district AI committee announcement).

So what: a short governance clause plus a weekly RFP/grant alert turns AI from a risk into a timely tool that saves staff hours and protects public trust.

Action	Owner	Timeline
Form cross‑department AI oversight body	City Manager / IT / Legal	Short‑term (1–2 years)
Pilot RFP parsing & grant monitoring prompts	Procurement / Grants Office	Immediate (weeks)
Contract clause: monitoring, red‑teaming, audits	Procurement / Counsel	Short‑term (1–2 years)
Staff prompt-writing & AI operations training	HR / Department Leads	1–3 months to enroll

“The idea of re-imagining our city, what the city looks like –whether it's the streets or public spaces, or the creation of green spaces – is very exciting,” says task force member Jorge Hernandez.