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

Lebanon's public sector is at a turning point: the newly formed Office of the Ministry of State for Technology and Artificial Intelligence (OMSITAI) has signed a strategic collaboration with Roland Berger to fast-track digitisation projects such as a national ID, digitised payments, and the governance needed for safe AI adoption - a practical move documented in the Roland Berger partnership report on Consultancy-me (Roland Berger partnership report on Consultancy-me).

At the same time, researchers from AUB stress that data science and open-data practices - from predictive analytics to airborne drone feeds for disaster response - can radically improve policy, service delivery and crisis preparedness in Lebanon (see the Research Features article on data science transforming Lebanon's public sector: Research Features: Data science transforming Lebanon's public sector).

For government teams looking to move from strategy to action, practical workforce upskilling such as prompt-writing and applied AI skills matters; Nucamp's AI Essentials for Work outlines a 15‑week curriculum to build those capabilities (see the Nucamp AI Essentials for Work syllabus: Nucamp AI Essentials for Work syllabus (15-week curriculum)), closing the gap between ambition and impact with concrete skills and tools.

“Our collaboration with Roland Berger, leveraging its extensive international expertise, will accelerate the adoption of artificial intelligence across vital sectors and support our plans to build a sustainable knowledge economy that strengthens Lebanon's position in this critical field.”

Selection of these top 10 prompts and use cases followed a practical, Lebanon‑centred filter: priority went to interventions that respect the socio‑technical lessons from AUB's LEAP review - where co‑creation, data democratization and low‑energy “micro‑model” alternatives are non‑negotiable - while also offering measurable operational value like the public‑service efficiencies highlighted in regional analyses.

Use cases were scored for (1) feasibility in a “data desert” and intermittent power context (favoring small‑data, edge or low‑compute models that can run on basic devices), (2) potential to reduce waste and expedite services (from citizen chatbots to procurement anomaly detection), (3) alignment with Responsible AI principles and human‑in‑the‑loop governance, and (4) workforce and partnership readiness - meaning projects that can scale with targeted upskilling and public‑private hubs (see a practical take on scaling skills and partnerships in Lebanon's public sector from Nucamp).

Final choices also leaned on demonstrated regional ROI and municipal policy precedents to ensure prompts map to realistic pilots and procurement paths, so teams can move from concept to a tested service that survives Lebanon's technical and institutional constraints.

For more on grounding AI in Lebanon's realities, read the AUB white paper coverage in L'Orient‑Le Jour and examples of public‑private scaling strategies.

“For LEAP to move beyond lofty aspirations, it must confront Lebanon's reality as a ‘data desert' facing a ‘talent exodus,' and embrace an AI approach co‑created with the broader public.”

A citizen-facing chatbot is one of the fastest, most practical AI pilots a Lebanese municipality can run: global peers show how these virtual assistants increase access while lowering routine workloads, from Egypt's KMT to the UAE's bilingual U‑Ask (see the HotTopics roundup of global government chatbot examples and AI governance insights: HotTopics global government chatbot examples and AI governance insights), and an open AI registry model - like the City of Lebanon's public AI Registry - demonstrates how transparency, impact tracking and staff training make adoption trustworthy (see the City of Lebanon public AI Registry transparency and impact tracking: City of Lebanon public AI Registry).

For Lebanon, a well‑designed chatbot can route permit queries, explain eligibility for services, and escalate complex cases to human agents while logging interactions for audit and improvement; the registry approach also shows practical benefits from pairing chatbots with governance: staff training and clear impact analysis (the registry even highlights productivity gains from adjacent AI tools, such as EMSReports.ai cutting documentation time from 40 to 10 minutes).

Prioritise Arabic language support, offline or low‑bandwidth fallbacks, and simple RAG safeguards so the service actually works for people across Beirut and smaller municipalities - turning a static website into a 24/7 front desk that preserves human oversight and builds public trust.

Yakshof turns scattered Arabic posts and news clippings into an actionable, real‑time pulse for Lebanese decision‑makers by combining social‑listening best practices with Arabic‑first sentiment models: platforms like AIM Insights Arabic sentiment analysis highlight dialect recognition and “real‑time analysis” as core features for tracking how citizens feel, while academic work shows that a Spark‑based pipeline with QARiB + Spark real-time Arabic tweet classification can classify live Arabic tweets into emotions and feed dashboards in seconds (see AIM Insights Arabic sentiment analysis for Arabic sentiment and the real‑time QARiB+Spark system).

That matters in Lebanon where political, economic and service‑delivery debates can spike overnight: Yakshof's value is rapid detection (so teams spot a brewing crisis before it trends widely), reliable dialect handling, and visualisations - from emotion pie charts to time‑series - so ministers and municipal teams see not just volume but nuance.

Implementation challenges are real - dialects, sarcasm, privacy and labeled data scarcity - but regional vendors and tools (and careful human‑in‑the‑loop review) already address many gaps, as Sensika media monitoring for the Arab world and industry media monitoring guides for the Arab world show - making Yakshof a practical, ethically mindful layer between public chatter and policy response.

For Lebanese municipalities looking to keep buses, service vans and mixed fleets on the road, dox packages predictive maintenance as a practical, cost‑saving service: by streaming vehicle telemetry into analytics you can spot battery or drivetrain anomalies weeks before a breakdown and schedule targeted repairs instead of costly emergency fixes.

The ODOS IoT Cloud platform shows how remote monitoring of 400+ signals (BMS temperatures, currents, SoC and vehicle‑network parameters) enables secure data collection, OTA harmonisation and predictive alerts across electric and conventional vehicles (ODOS IoT Cloud EV battery telemetry platform).

Geotab's overview of predictive maintenance explains the operational wins - reduced downtime, prioritized repairs, longer asset life - and the real implementation needs: the right sensors, staff training and careful data capture (Geotab predictive maintenance overview and operational guide).

Teams can accelerate pilots using multi‑modal public datasets and federated strategies - EVIoT's telemetry corpus illustrates the signals and labels useful for failure prediction and RUL modelling - so fleets in heat‑sensitive climates can detect thermal stress early and avoid stranded vehicles, smoother charging schedules and predictable maintenance budgets (EVIoT predictive maintenance telemetry dataset on Kaggle).

NAR's primer on drone technology makes a simple promise for Lebanon's built‑asset backlog: faster, safer inspections and automated, audit‑ready reports that replace scaffolding and lane closures with high‑resolution imagery and thermal checks (NAR drone technology guide for infrastructure inspections).

For municipalities and public works teams, that means routine roof and facade surveys, port and bridge overviews, and rapid post‑storm damage snapshots can be turned into 3D meshes, geotagged findings and shareable PDFs within hours using established workflows - exactly the export options shown in the ArcGIS Drone2Map inspection tutorial that walks through creating inspection schemas, marking features in 3D and generating HTML/PDF reports for crews and contractors (ArcGIS Drone2Map infrastructure inspection tutorial).

Combined with industry guides on infrastructure inspection and AI‑assisted defect detection, drone programs become a practical tool for Lebanese teams to increase inspection frequency, prioritise repairs by risk and keep engineers safely on the ground while producing repeatable, auditable records that speed maintenance decisions (Skydio guide to drones for infrastructure inspection).

“The Power Authority is proud to be leading the way in the advanced deployment of automated flight technologies for use in the utility industry. Drones will become an even more valuable tool as we expand our capability to detect infrastructure issues and support our mapping and land management responsibilities.”

Eqlim - a Beirut‑based risk‑intelligence service founded in 2013 - turns messy, multilingual signals into practical early warnings that Lebanese emergency planners and procurement teams can actually use: the Tracxn company profile notes EQLIM curates 100 timestamped, geocoded event types drawn from 50,000+ web and social sources in 15 languages, focused on unrest, regulatory changes, infrastructure outages and other shocks that ripple through supply chains (Eqlim company profile on Tracxn).

Coupling that stream with proven AI playbooks - like the DLA's framing of AI for SCRM, which uses models to simulate equipment failures, weather or route interruptions - lets Lebanese teams move from reactive scrambling to scenario‑based contingency plans and supplier substitution lists (DLA white paper on AI for supply chain risk).

The payoff is concrete: spotting the social‑media tremor of a port disruption or fuel depot outage days earlier so municipal logisticians can reroute critical deliveries before queues and cascading shortages form.

“DLA's efforts showcase how AI-driven analytics enhance accountability, streamline investigations and preempt supply chain threats.”

Procurement fraud is a stealth drain on public budgets, and Lebanese ministries and municipalities can get ahead by combining pragmatic controls with machine‑learning anomaly detection: start with simple business rules and scale into hybrid analytics that blend anomaly detection, link analysis and historical peer‑grouping so investigators focus on real risks rather than noise.

Techniques such as isolation‑forest or related binary‑tree partitioning - an approach explained in the EPJ Data Science procurement-fraud mapping - are well suited to sparse or high‑dimensional procurement logs, while the SAS playbook for government fraud detection recommends layering anomaly signals with profiling and text‑mining to spot collusion, split purchase orders and suspicious vendor ties before payment.

Regional practitioners don't need perfect data to begin: a staged rollout - data integration, unsupervised anomaly models, explainable risk scores and human‑in‑the‑loop review - mirrors the five‑step analytics path shown in industry guidance and has delivered fast wins (one manufacturer realised a 42% incremental recovery and one‑month payback after deploying split‑PO detection).

For Lebanon, the practical so‑what is clear: targeted ML alerts turn thousands of raw invoices into a short, prioritized queue for auditors, cutting investigation time and keeping scarce public funds where they belong (see more in the SupplyChainBrain primer on preventing procurement fraud and the SAS hybrid-analytics framework for fraud detection).

Policy teams in Lebanon can shortcut weeks of drafting by using Gemini-style prompt templates to generate tight regulatory impact summaries, stakeholder analyses and decision-ready briefings - as practical playbooks show, strong prompts combine a clear persona, explicit task, relevant context and a required output format so the model knows exactly what to deliver (see Google's Gemini prompt design guide for strategy and examples: Google Gemini prompt design guide - prompting strategies and examples).

Templates and few‑shot examples can turn raw legislation, consultation responses and budget notes into an executive summary, a ranked list of affected stakeholders, and suggested mitigation options in minutes; in one live test a 221‑word prompt produced a 17‑page privacy‑risk report in under four minutes, illustrating both the speed and the

so what?

impact for lean teams that must move quickly (DataGrail AI privacy prompt test - 17-page privacy-risk report in under four minutes).

Pair generation with evaluation templates and human review - use metric rubrics and iterative prompts to score grounding and clarity, and avoid over‑reliance on facts the model may invent (practice shown in prompt galleries and summary templates: PromptLayer templates for executive and policy summaries - summarizing long reports).

This approach yields repeatable drafts ready for legal review, stakeholder outreach and public consultation while keeping human oversight central.

Targeted public‑communication campaigns in Lebanon should marry old‑school messaging craft with smart, repeatable prompts: start by writing in the official voice of the campaign or service, foreground real people's stories, and choose one clear message to repeat - techniques drawn from David Axelrod campaign messaging strategies in a MasterClass article (MasterClass: David Axelrod campaign messaging strategies for persuasive campaigns).

For delivery, SMS remains the most direct channel (think short, 160‑character nudges with ~98% open rates), but only when paired with consent, easy opt‑outs, thoughtful timing and two‑way options so residents can reply or ask questions; these operational rules are central to municipal rollouts and inclusion strategies in local government communications (PublicInput guide to SMS text messaging best practices for local governments).

In practice, AI marketing prompts can auto‑generate concise intro texts, GOTV reminders and segmented CTAs, while humans validate tone, legal compliance and multilingual phrasing - so a single well‑timed 160‑character reminder can land in front of most residents and turn information into action.

Combine Eqlim's geocoded, multilingual event stream with live asset‑tracking and AI‑assisted dispatch prompts and Lebanese emergency teams get a practical, speed-first toolkit: Eqlim's timestamped alerts (see the Eqlim company profile on Eqlim company profile on Tracxn startup database) feed into a real‑time operations prompt that asks a dispatcher‑facing model to rank nearby resources by ETA, fuel, and specialist capability; pair that with GPS and RTLS feeds (like the asset‑tracking playbooks from Link Labs asset tracking playbook for emergency response or GPS case studies from Geoforce GPS case studies for disaster recovery) and the system can suggest which generator, crew or convoy to reroute before queues, outages or roadblocks cascade.

Add a performance‑management dashboard to monitor response times and bottlenecks (see practical metrics in the Zencity performance management strategies for emergency response) and prompts become actionable SOPs: short, repeatable instructions for dispatchers (“send Unit A via Route X; notify Port Ops; preposition spare fuel at Shelter Y”), turning a social‑media tremor about a port disruption into a reroute that prevents hours of delay and protects lives and services.

“Where should first responders be based in order to respond to the most likely sites of accidents? Is it always best for the closest first responders to go to an accident site, or - depending on traffic - could it be faster for first responders who are farther away to respond?”

Urban planning and transport optimisation in Lebanon can get a practical boost from pixel‑based, big‑data visualisations that turn streams of speed and flow readings into instantly readable maps: TU Delft's pixel‑plot approach shows how a single screen - using color for speed and rectangle size for flow - lets humans spot purple bands of traffic jams, bright orange spikes of heavy flow, weekday/weekend patterns and even missing data in the blink of an eye (pixel-based traffic visualisation tutorial).

For Lebanese cities facing intermittent data and constrained budgets, the technique scales from dense sensor networks to sparser feeds (traffic counters, mobile probes or periodic surveys) so planners can prioritise problem corridors, tune signal timings, and justify targeted interventions with a clear visual story - turning raw logs into a compact picture that decision‑makers and communities actually understand.

Pairing these visualisations with local data hubs and skills programmes through public‑private efforts like a Lebanon Data Science Hub helps ensure the insights lead to actions, not just charts (Lebanon Data Science Hub and public‑private scaling).

Getting started in Lebanon means pairing bold strategy with small, practical steps: use the OMSITAI–Roland Berger collaboration to pick two high‑value pilots (for example a citizen chatbot and a procurement anomaly detector), protect a focused seed budget (as the AI minister has argued, “$30 to $50 million” can kickstart digital ID, payments and core infrastructure), and invest in rapid workforce upskilling so teams can own deployments and audits; the Nucamp AI Essentials for Work is a 15‑week, practice‑focused path that teaches prompt writing and applied AI skills for non‑technical staff (Roland Berger and OMSITAI AI partnership in Lebanon, Lebanon AI minister Kamal Shehadi on $30–$50M government AI funding, Nucamp AI Essentials for Work bootcamp syllabus (15-week)).

Start small, require human‑in‑the‑loop reviews and open registries for transparency, then scale what demonstrably cuts cost or time; a single, well‑run pilot that saves staff hours or prevents disruption can change public trust almost overnight - like moving a paper counter into a 24/7 digital front desk that still seats a human agent for complex cases.

“Our collaboration with Roland Berger, leveraging its extensive international expertise, will accelerate the adoption of artificial intelligence across vital sectors and support our plans to build a sustainable knowledge economy that strengthens Lebanon's position in this critical field.”