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

Taiwan's government has turned up the scale on public‑sector AI: the “AI New Ten Major Construction” (often called the Ten Major AI Infrastructure Projects) channels roughly NT$200 billion and targeted investments to build sovereign AI, national computing power and three core technologies - silicon photonics, quantum and smart robotics - while pushing AI into smart healthcare, transport and a “nationwide smart living circle” so millions of firms can adopt AI and platform software can grow into exportable industries; the effort explicitly echoes the 1970s Ten Major Construction and even sets sights on multi‑trillion‑NT dollar economic impact by 2040.

The plan pairs large hardware bets with talent and data governance work (including a sovereign Traditional Chinese training corpus) even as AI legislation and risk‑tiering debates continue in the legislature.

Practical workforce preparation matters: for front‑line civil servants and R&D teams, hands‑on courses like Nucamp's 15‑week AI Essentials for Work help translate policy goals into usable prompt and deployment skills - see the government overview at TechSoda and coverage in the Taipei Times for details.

“overtake on the curve,”

Attribute	Details
Program	AI Essentials for Work (15 weeks)
Description	Practical AI skills for any workplace; prompts, tools, and job‑based AI applications
Cost	Early bird $3,582; regular $3,942 - paid in 18 monthly payments
Syllabus / Register	AI Essentials for Work syllabus • AI Essentials for Work registration

Selection of the Top 10 prompts followed a practical, policy‑aware rubric rooted in Taiwan's evolving governance landscape: prompts were scored for alignment with the draft AI Basic Act's core principles (human autonomy, privacy, transparency and fairness), mapped to a risk‑tiering approach the Ministry of Digital Affairs is being asked to develop, and screened for feasibility given Taiwan's data and computing assets such as TAIWANIA 2 and the TAIDE initiative for Traditional Chinese models.

Priority was given to prompts that enable measurable public‑service gains - examples supported in government guidance and Nucamp research include streamlining patient workflows to cut administrative costs in public hospitals - while remaining testable inside regulatory sandboxes or pilot settings.

The methodology also weighed cross‑agency implementability (so a prompt that aids both health and social services scored higher), stakeholder acceptability, and training lift for civil servants so adoption scales without surprise harms; sources informing this process include an AmCham explainer on Taiwan's draft AI Basic Act and the NSTC/agency summaries of the draft bill and its seven guiding principles.

“Early communication with stakeholders is crucial,” they say.

Policy makers are converging on a practical, risk‑tiering approach - one that asks MODA or a central coordinator to act like a traffic‑controller for AI, sorting systems by harm potential and handing enforcement to sector regulators - yet the draft leaves the “who flips the switch” question unresolved.

Taiwan's draft AI Basic Act explicitly tasks the Ministry of Digital Affairs with building a risk framework that aligns with international models (notably the EU's tiered logic) while allowing industry‑specific authorities to set detailed rules, certification and testing requirements; stakeholders therefore urge clear role‑allocation, cross‑agency protocols and regulatory sandboxes so pilots can proceed without legal whiplash.

A workable brief for government teams prioritises: (1) a transparent risk taxonomy tied to concrete checks for high‑risk public‑service uses; (2) interoperable evaluation and labeling mechanisms for products that may affect health, finance or civic rights; and (3) fast‑path experimental zones to surface real‑world harms before scaling.

For background on the draft's responsibilities and the proposed MODA role see Taiwan's draft AI Basic Act coverage at AmCham and the analysis of MODA's risk‑based authority by Lee and Li.

“Early communication with stakeholders is crucial,” they say.

For Taiwan's R&D and AI professionals, a pragmatic six‑month training plan pairs executive strategy with hands‑on labs: a leadership track (six months of live modules and strategy playbooks like Wharton's Leadership Program in AI and Analytics) layered over technical pillars - foundation ML, prompt engineering, MLOps and deployment - drawn from intensive professional certificates and bootcamps; practical examples include Berkeley Executive Education's applied modules and capstone work and MIT xPRO's short immersive courses for data engineering and AI productization.

Add a bootcamp-style lab sequence and vendor tool practice (think IBM's AI Developer Professional Certificate or a 300‑hour bootcamp) plus a two‑week capstone that targets a real public‑sector use case such as streamlining patient workflows or piloting an AI‑assisted Traditional Chinese chatbot in a municipal clinic.

Scale the program with an LMS and microlearning strategy to keep completion high, then certify outcomes with project demos so agencies see concrete ROI - one memorable metric: a capstone that moves from code to a live pilot within 90 days proves the training works.

“In a rapidly evolving business landscape, executives who grasp AI fundamentals can proactively identify opportunities and mitigate risks, ensuring their organizations stay ahead of the curve. The Leadership Program in AI and Analytics from Wharton Executive Education provides senior executives with a strong foundation in AI/generative AI (genAI) through core concepts and techniques, while also allowing them to hone their leadership skills, helping them drive successful business outcomes.”

A sovereign Traditional Chinese AI training corpus is shaping up as a practical cornerstone of Taiwan's AI push: MODA is inventorying language data across agencies and drafting standard licensing terms so public and private stakeholders can safely contribute and apply the resource, with the first release targeted for Q4 2025; see the ministry's plan for developing the corpus and data governance measures and TechSoda's coverage of the broader NT$200 billion “AI New Ten Major Construction” vision.

The corpus will pull from open government data, policy reports and publications and extend to Hakka, Indigenous and cultural‑heritage materials to anchor models in Taiwanese contexts, while datasets will be quantified by tokens rather than raw volume - a timely detail given only about 1,000 of more than 50,000 open datasets are currently textual.

Licensing drafts and a public comment window aim to resolve copyright and privacy questions so agencies, startups and researchers can access a vetted, sovereign dataset that supports Traditional Chinese LLMs and local public‑service pilots; early access will be application‑based, balancing openness with stewardship.

Attribute	Details
Planned launch	Focus Taiwan report: Taiwan's sovereign Traditional Chinese AI corpus planned for Q4 2025
Content types	Open government data, policy reports, govt publications, Hakka, Indigenous, cultural/historical/geographic
Licensing	Standard terms being drafted; public/private access by application
Scale note	Datasets quantified by tokens; ~1,000 of 50,000+ open datasets are textual

“train more AI models with Taiwanese perspectives”

A pilot “Smart Living Circle” for a population of roughly 200,000 should stitch together practical, testable pieces: prioritize AI Essentials for Work - practical smart healthcare deployments that streamline patient workflows and cut administrative costs in public hospitals, staff up with dedicated AI Essentials for Work - data stewardship and dataset governance roles to curate, label and protect datasets for trustworthy local models, and embed experiments in formal AI Essentials for Work - AI sandboxes and public-sector test fields so public‑sector pilots can iterate safely.

Operationally, the blueprint pairs frontline efficiency gains with governance: lightweight MVPs for admin and triage first, clear stewardship responsibilities next, and sandboxed scaling to uncover boundary conditions - an approach that turns national AI investments into neighborhood‑level services citizens actually use and trust.

Turning Taiwan's national computing ambitions into a bankable public‑private partnership starts with a clear, customizable RFP template that spells out purpose, timelines and the procurement model, and Info‑Tech's Infrastructure Service RFP Template is a practical starting point - it covers co‑location/hosting, technical and managed services, IaaS capabilities, security & privacy, and resiliency & recovery while prompting teams to define engagement methodology and vendor qualifications up front (Info-Tech Infrastructure Service RFP Template for Infrastructure, IaaS, Security & Resiliency).

For a sovereign computing PPP, the RFP should explicitly require how bidders will support national goals (sovereign model training, sandboxed public‑sector pilots), state whether the award is winner‑takes‑all or a two‑stage process, set a vendor Q&A schedule, and adapt Section 5 (scope of work) to include data stewardship and secure multi‑tenant operations so projects don't stall in procurement rework; pair that with a formal scoring tool to make decisions auditable and defensible.

For practical guidance on aligning procurement with safe pilot zones and test fields, see Taiwan's AI Sandboxes and Test Fields Playbook for Government AI Pilots (Taiwan AI Sandboxes and Test Fields Playbook).

RFP Component	Template guidance
Purpose & Goals	Insert the purpose and goals of acquiring service
Timeline	Insert RFP issue and award process timeline
Procurement model	Decide winner‑takes‑all or two‑stage RFP
Scope of Work	Modify Section 5 for co‑location, IaaS, security, resiliency
Vendor Q&A	Indicate how vendor questions will be handled and event schedule
Scoring	Use Info‑Tech's Infrastructure Outsourcing RFP Scoring Tool

Taiwan's regulatory sandbox ecosystem is steadily maturing into a practical fast‑lane for both FinTech and UAV/AI pilots: the proposed Bill explicitly aims to create “a so‑called ‘regulatory sandbox'” where innovative financial services can be trialed without immediate regulatory fallout, and scholars note Taipei's aggressive use of sandbox regulation has extended beyond finance to unmanned vehicles and AI pilots (NatLaw Review article on Taiwan regulatory sandbox bill, Cornell SSRN analysis of sandbox diffusion).

Early, concrete wins include the Financial Supervisory Commission's approval of EMQ - whose remittance pilot targets Indonesian, Vietnamese and Filipino migrant workers and arrived as migrant remittances topped more than US$3 billion in 2018 - demonstrating how a fintech pilot can deliver real social value while staying time‑boxed under sandbox rules.

Practical playbooks for government teams recommend pairing these pilots with local test fields and clear data‑steward roles so UAV AI trials (think safe, instrumented corridors for unmanned delivery or inspection) and fintech experiments both surface risks before scale; see Taiwan's public‑sector sandbox guidance for hands‑on implementation (AI sandboxes and test fields guide - Nucamp AI Essentials for Work syllabus).

Item	Detail / Source
First approved sandbox startup	EMQ - remittance services for migrant workers (Kapronasia)
Remittance context	US$3 billion remitted by migrant workers in 2018 (Kapronasia)
Sandbox duration	Up to 1 year; +6 month extension; possible up to 3 years if law changes (Kapronasia)
Cross‑sector use	Applied to financial services, unmanned vehicles, and AI (SSRN)

“Taiwan is one of the most important strategic growth markets for EMQ and the regulatory approval from the FSC represents a significant milestone for our operation in Taiwan,” Max Liu, co‑founder and CEO of EMQ, said.

Scaling AI across Taiwan's millions of small and medium enterprises starts with the practical building blocks already flagged by government plans: demand‑driven talent matching, AI pilot projects, data platforms, test fields and regulatory co‑creation that lower the barrier from prototype to production.

Taiwan's “AI for Industrial Innovation” playbook calls for one‑stop support through IoT Integration Service Centers (IISC) and open research platforms so an electronics shop or food manufacturer can access templates, tooling and dataset stewardship without reinventing the stack; see the Taiwan Ministry of Economic Affairs AI for Industrial Innovation action plan for details.

Pair those services with regional hubs and international partnerships - encouraging global AI research centers to anchor local ecosystems - and a staged rollout that uses local pilots and test fields to de‑risk deployments.

Capacity levers already in play (graduating tens of thousands of AI specialists annually, retraining programs and targeted funding) create a realistic scaling path: hit regional milestones (the national strategy targets roughly 200,000 firms transformed by 2028) and then multiply learnings across six regional development zones to reach broader adoption.

The result is a low‑friction route for SMEs to move from curiosity to useful automation - anchored by data stewardship roles and sandboxed pilots so value shows up in payrolls and shop floors, not just slide decks; read more on Taiwan's national AI strategy and ambitions.

A public‑service chatbot in fluent Traditional Chinese can make government services reachable and reliable for everyday citizens by combining retrieval‑augmented generation (RAG) with locally grounded datasets and clear stewardship roles: RAG helps the model cite policy documents and plain‑language guidance rather than inventing answers (PLOS One study on RAG and LLMs for policy understanding), while attention to sociocultural context ensures responses respect local norms and idioms - a point Jing Cheng stresses when arguing AI governance must reflect national and cultural realities (Contextualizing China's AI governance by Jing Cheng - Global Policy Journal).

Operationally, these chatbots should sit inside government sandboxes and be supported by trained data‑steward teams who curate Traditional Chinese corpora and oversee privacy and labeling, turning pilots into trustworthy services that can, for example, reliably guide a patient to the right clinic form or flag when a human caseworker is needed (Nucamp AI Essentials for Work syllabus - smart healthcare pilots and data stewardship).

The combined approach - technical retrieval, cultural tailoring, and formal stewardship - keeps answers accurate, auditable and culturally resonant for Taiwan's citizens.

Component	Supporting source
RAG for policy‑accurate answers	PLOS One study on RAG and LLMs for policy understanding (2024)
Local cultural context matters	Contextualizing China's AI governance - Jing Cheng, Global Policy Journal (2023)
Data stewardship & healthcare pilots	Nucamp AI Essentials for Work syllabus - smart healthcare pilots and data stewardship

Taiwan's cross‑sector roadmap stitches silicon photonics, quantum tech and smart robotics into a single industrial sprint: silicon photonics - already framed as a national priority by the NDC - is being advanced through the SEMI Silicon Photonics Industry Alliance and its three SIGs to tackle design, advanced packaging and equipment so co‑packaged optics and 3D photonic engines can relieve AI data‑center bottlenecks, while quantum and autonomous robotics are positioned as complementary “economic shields” for hardware+software leadership.

Anchored by TSMC, ASE and more than 110 industry partners, the SiPhIA effort maps a 2024–2027 path from 2D to 2.5D/3D integration to cut energy per bit and boost bandwidth, and government planning sees this feeding into Taiwan's wider Ten Major AI Infrastructure agenda; readers can explore the SiPhIA roadmap on the SEMI Silicon Photonics Industry Alliance website (SEMI Silicon Photonics Industry Alliance roadmap) or the government's prioritization of silicon photonics on Focus Taiwan (Focus Taiwan government silicon photonics prioritization).

The commercial prize is tangible - analysts eye a multi‑billion dollar silicon‑photonics market - and the vivid payoff is simple: moving “from copper to light” rewrites how whole data centers consume power and how robots, sensors and quantum links will talk to each other across the island's advanced packaging ecosystem.

Initiative	Key fact
SEMI SiPhIA SIGs	Three SIGs for systems, packaging/testing and equipment; >110 partners (industry & research)
Market & policy	Govt priority under Ten Major AI Infrastructure; global SiPh market forecast ~US$7.8B by 2030
Cross‑sector links	TSMC/ASE optics + Hon Hai moves into quantum/robotics - packaging/integration focus

“The SEMI Silicon Photonics Alliance, centered on Taiwan's semiconductor industry, brings together over 110 leading domestic and international companies to develop the world's largest and most comprehensive international platform for silicon photonics technology collaboration. The alliance spans the entire supply chain, integrating cross-enterprise and cross-disciplinary expertise to fuel global innovation. Additionally, it has launched three SIGs focused on driving innovative technology breakthroughs and accelerating standardization efforts to jointly address the challenges posed by technological fragmentation.”

Privacy and ethics must be baked into any Taiwan AI healthcare pilot from day one by using a formal Privacy Impact Assessment (PIA) to map data flows, catalogue sensitive health attributes and document mitigation - PIAs act as a decision tool that makes data collection transparent and provable, as the U.S. DHS guidance explains (U.S. Department of Homeland Security guidance on Privacy Impact Assessments).

For AI systems that touch patient records, PIAs should go beyond checklists and adopt AI‑specific assessments that cover bias, explainability and lifecycle monitoring; practical tool guidance and selection criteria are available in recent industry primers on AI PIAs (AI privacy impact assessment tool selection guide for businesses).

To preserve both utility and trust, pilot designers should pair governance with technical defences - differential privacy, federated learning and synthetic data - to reduce re‑identification risk while keeping models useful, a balance explored in healthcare privacy playbooks and vendor risk platforms (Balancing privacy and utility in healthcare AI - Censinet perspective).

Remember the hard lesson: de‑identification is not a magic wand - advanced analytics can re‑identify records unless safeguards are tested, documented and updated as models evolve, and PIAs provide the governance record auditors and citizens will demand.

“some data, particularly peoples' sensitive health data . . . is simply off limits for model training.”

Taiwan's next steps should stitch the current “guidance‑before‑legislation” approach into a clear action plan: move the Draft AI Act toward legislative review while empowering MODA to finish a risk‑tiering framework that hands sectoral oversight to domain regulators and keeps regulatory sandboxes active for time‑boxed pilots (see the Taiwan AI Action Plan and Draft AI Act overview at STLI).

At the same time, boost practical incentives and fast‑track talent pipelines so the island's hardware and software bets actually land as useful services - simple wins like smart‑healthcare pilots that reliably route a patient to the right clinic form, not just slide decks, prove the payoff.

Legal clarity from Lee and Li's practice guide on accountability, data governance and PIAs will help agencies balance openness with IP and PDPA constraints, while targeted training (scaleable bootcamps and upskilling) seeds data‑steward roles and sandboxes with people who can deploy responsibly.

Pair these policy moves with procurement for sovereign data and compute, robust PIA practice, and SME incentives so Taiwan converts its Ten Major AI Infrastructure projects into neighborhood services citizens trust and exportable AI products that meet global standards.

Attribute	Details
Program	AI Essentials for Work (15 weeks)
Description	Practical AI skills for any workplace: tools, prompt writing, job‑based applications
Cost	Early bird $3,582; regular $3,942 - paid in 18 monthly payments
Syllabus / Register	AI Essentials for Work syllabus • Register for AI Essentials for Work