Top 10 AI Prompts and Use Cases and in the Government Industry in Czech Republic

The Czech Republic's push to embed AI into public services makes well‑crafted prompts more than a technical nicety - they are the bridge between law, policy and everyday citizen interactions.

National plans like the National AI Strategy 2030 are steering investment, testing environments and public‑service pilots that demand transparent, safe AI; at the same time funding schemes such as TWIST (grants up to CZK 30 million) and even oversubscribed Deep Tech calls show strong demand and urgency.

Clear prompts speed deployment of chatbots, action pages, labour‑market forecasting and predictive maintenance while helping teams comply with the EU AI Act and national implementation steps.

Practical prompt technique - clarity, context, role definition and iteration - is therefore essential for government teams and vendors (see best practices for crafting prompts) to turn strategy into reliable, citizen‑facing services.

“The advent of artificial intelligence represents a significant opportunity for the transformation and modernisation of Czech industry. That is why we at the Ministry have decided to assume the leading role in implementing AI into the Czech legal system and to actively support its development and practical application.”

The methodology behind this Top 10 list mixes policy alignment, legal caution and practical funding realities: prompts and use cases were selected by cross‑referencing the National AI Strategy 2030 and its Action Plan framing, EU‑level obligations under the EU AI Act, and on‑the‑ground programmes such as TWIST and OP‑TAK to ensure every prompt targets realistic government workflows (grant evaluation, policy drafting, labour‑market forecasting, chatbot design and predictive maintenance).

Sources include the Ministry of Industry and Trade's NAIS 2030 summary and implementation notes, OECD policy metadata on NAIS governance, and regulatory trackers that map how Czech authorities plan to implement the AI Act and set up sandboxes and market surveillance.

Prompts were prioritised where NAIS 2030 lists clear public‑service use (seven strategic pillars), where funding or testbeds exist (TWIST grants, OP‑TAK calls) and where regulatory risk is highest so prompts can include compliance cues; the Deep Tech call's 200% oversubscription was used as a signal to emphasise scalability and reproducibility in prompt outputs.

To use the prompts: state the intended public authority, cite the data scope and risk class (per EU AI Act), require sources and explainability, and iterate in a test environment or sandbox before production.

For further background, see the Ministry of Industry and Trade: National Artificial Intelligence Strategy 2030 (NAIS 2030) announcement and OECD guidance on national AI strategy implementation.

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life. In order to use this potential to the maximum for the benefit of the Czech Republic, we have prepared the updated National Artificial Intelligence Strategy of the Czech Republic 2030.”

Bridging EU law and Czech practice, the Ministry of Industry and Trade (MPO) is now the lead architect for implementing the EU AI Act in Czechia - a shift from paper to practice that means designating authorities, standing up conformity assessment pathways and running a regulatory sandbox so innovators can test real systems under supervision.

Member States must name national authorities by 2 August 2025 (see the EU implementation timeline and national-designation overview), and Prague's approved Draft Implementation assigns clear roles: MPO as coordinator, the Czech Telecommunication Office for market surveillance, ÚNMZ as the notifying authority and the Czech Standards Agency to run the sandbox so projects (for example autonomous systems or advanced analytics) can be trialled under regulator oversight.

That mix of legal alignment, practical testing and funding (including targeted support for SMEs and conformity bodies) is designed to keep Czech industry competitive while making citizen‑facing AI auditable and explainable - imagine a startup running a high‑risk prototype in a supervised testbed rather than live on the streets.

For the government's approval and institutional details, see the UNMZ announcement.

“Our goal is to create a transparent and quality environment in the Czech Republic that will allow only trustworthy and competent entities to certify AI systems according to the rules of the European Act on Artificial Intelligence.”

The January 15, 2025 US decision to tighten AI‑chip exports - which placed Czechia in a stricter, second‑tier category - has immediate regulatory and operational consequences for Prague's AI planning: ministries and research centres now need rapid regulatory impact assessments and scenario analyses to understand licence caps, supply delays and how constrained access to advanced GPUs would affect model training and EU supercomputing projects (Bloomberg: US will limit AI chip exports to Czechia).

European commentators warn these limits could impede planned supercomputers and AI testbeds unless Brussels and Washington find common ground (Science|Business: EU supercomputers and export risks from US export restrictions), while policy analysis highlights the need for allied coordination, tailored end‑use controls and mitigation options such as investment in CUDA‑alternative stacks, staged procurements and stronger supply‑chain due diligence (CSIS analysis: allied export‑control authorities and options).

The practical “so what?” is stark: without scenario plans, grant‑funded pilots and municipal predictive‑maintenance projects risk becoming constrained by a shortage of the very GPUs that power large‑scale AI, turning high‑profile compute ambitions into idle racks.

TWIST and OP‑TAK funding create a practical runway for Czech public bodies to pilot citizen‑facing chatbots, but successful deployments need clear prompts, risk labels and operational rules rather than tech optimism alone: an academic study of Czech public administration lays out a step‑by‑step approach for introducing chatbots into central state offices and predicts they will become a core part of modern public relations (Academic study: Chatbots in Czech public administration), while market data from Smartsupp shows the user dynamics any municipal chatbot must handle - about 20.5% of visitors are reached by chatbots, weekend traffic accounts for a meaningful share of queries and many conversations are designed to hand off to a human agent, so prompts should include escalation rules, working‑hours expectations and explainability for officials and citizens (Smartsupp 2023 chatbot and live chat trends in the Czech Republic).

That combination - grant‑backed pilots, rigorous onboarding guidance from the ijpamed analysis, and prompt templates that specify data scope, risk class and handover logic - reduces the chance that a high‑profile pilot becomes a silent weekend inbox: over 14% of initiated conversations occur outside standard weekdays, a figure that makes “always‑on” design choices a genuine public‑service consideration.

TWIST grants demand not just technical ambition but a defensible, transparent evaluation process: using analytic grant rubrics - built around validity, reliability, fairness and efficiency - lets reviewers score proposals consistently and surface anomalies that can signal fraud or mismanagement (for example, budgets that don't align with timelines or vague evaluation plans) while protecting against bias and ad‑hoc decisioning; practical guidance on rubric design and calibration helps keep panels aligned and makes scoring a useful discussion tool rather than a black‑box veto (see how to design effective rubrics and rubric best practices).

A dedicated grants rubric that weights project goals, timeline, impact, evaluation and budget gives Czech public funders a repeatable checklist to flag risky submissions and to require clearer documentation from applicants, reducing the chance that a high‑value TWIST award becomes a compliance headache rather than a pilot success - use the example grant evaluation rubric as a template for scoring and escalation rules.

The Ministry of Labour and Social Affairs must turn NAIS-aligned ambition into operational forecasting and targeted reskilling plans: recent reporting estimates generative AI will affect over 2.3 million Czech workers - more than four in ten jobs - so demand will outstrip generic training unless programmes are tightly focused (Study: Generative AI will affect over 2.3 million Czech workers - Expats.cz); the Czech National Bank highlights that 35% of employment is in occupations with higher automation risk, meaning policy should prioritise low‑risk

hybrid intelligence

skills, data‑quality roles and supervision tasks rather than one‑size‑fits‑all digital courses (Czech National Bank: The impact of AI on the labour market).

At the EU level commentators call for an

AI Social Compact

that pairs investment with income protection and reorientation pathways - practical measures for Prague include demand‑driven forecasting, apprenticeships tied to municipal pilots, and clear metrics for success so policymakers are not surprised when

four in ten

becomes four in the unemployment line rather than four in re‑skilled careers (EPC: AI's impact on Europe's job market - call for a social compact).

For Czech public bodies implementing NAIS 2030, the EU AI Act's transparency and explainability rules turn abstract principles into practical deliverables: high‑risk systems must publish clear, comprehensible information about capabilities, limitations and human‑in‑the‑loop arrangements, users must be informed when they're interacting with AI, and providers of foundation models face technical‑documentation and reporting obligations that feed audits and conformity assessments (see the EU's explainability notice for the Publications Office of the EU).

That means ministries, municipal pilots and TWIST‑funded chatbots should plan explainability reports, audit logs and easily digestible user notices as part of launch checklists - not after a problem appears - so a Prague resident can see why an automated eligibility check flagged their application rather than getting an opaque rejection.

Practical XAI methods help meet these obligations by mapping inputs to decisions and surfacing limitations, while whitepapers and guidance explain the compliance trade‑offs between model complexity and interpretability; early adoption of these practices will make Czech deployments auditable, defensible and easier to explain to citizens and auditors alike (read a practitioner view on XAI and the AI Act and a detailed ISACA primer on next steps for compliance).

Smart Quarantine and Mapy.cz show how Czech AI tools can power precise public‑health surveillance and faster emergency response: NAIS documentation and the EU AI Watch note an AI‑based Smart Quarantine and an AI‑enabled COVID‑19 chatbot alongside Mapy.cz location alerts that aim to flag potential risky encounters and correlate anonymous positive‑case data for contact tracing (EU AI Watch report: Czech Republic AI Strategy).

Early pilots - starting in South Moravia and planned for Prague - illustrate the practical payoff: better targeted quarantines can reopen parts of the economy without resorting to blunt, economy‑wide shutdowns, but success depends on clear privacy rules, short data retention and public trust.

Embedding prompt templates that specify anonymisation, retention limits and escalation paths into chatbots and trace‑analytics pipelines will help municipal teams balance speed, oversight and citizens' rights while making emergency responses auditable and repeatable (Ole Jann article on Successful Smart Quarantine in the Czech Republic).

“The information absolutely needs to be protected, to be kept secret from everybody else, there needs to be a clear outline when the data will be destroyed and people need to know for what purposes it will be used.”

Prague's municipal infrastructure is a perfect fit for AI-driven predictive maintenance: a city example already in action is Neuron Soundware's project with DPP - Prague's subway moves a million passengers a day - where an acoustic IoT system fitted 21 escalators with 189 sensors that monitor sound, run edge AI for anomaly detection and send real‑time email/SMS alerts and repair recommendations to technicians (Neuron Soundware predictive maintenance case study for DPP escalators).

The approach - edge processing to avoid terabyte cloud transfers, component‑level diagnostics and prioritized work orders - translates directly to roads (embedded vibration and strain sensors), water networks (pressure and leak anomaly detection) and public buildings (HVAC and structural health monitoring).

Empirical studies of urban transport systems suggest predictive models can cut maintenance costs by roughly 30% and prevent about 92% of unexpected failures, which means fewer emergency repairs, longer asset life and less disruption for citizens (Intelligent Infrastructure for Urban Transportation predictive maintenance research (Brilliance 2024)).

The takeaway is simple: modest sensor deployments and well‑scoped prompts for anomaly detection turn reactive budgets into planned workstreams, keeping Prague's streets, pipes and public buildings working before anything breaks.

Czech CERTs and municipal SOCs juggling thousands of daily alerts can use AI as a practical force‑multiplier: AI‑driven detection engineering automates log analysis and correlation so analysts stop drowning in noise and start hunting the real threats, while smart triage surfaces high‑fidelity incidents and frees scarce expertise for deep investigations (Prophet Security's practitioner analysis shows AI can both automate routine tasks and sharpen detections, turning tens of thousands of alerts into a manageable queue).

Tools that summarise context in plain language and pre‑correlate evidence can cut triage time dramatically - Corelight's Guided Triage reports up to a 50% reduction in time to triage - so Czech teams can prioritise response, preserve audit trails and meet tighter reporting expectations.

Paired with structured AI‑aware intake workflows and specialist training (for example certified AI‑forensics courses), these approaches help translate noisy feeds into fast, defensible incident response that keeps critical services running rather than leaving GPUs and logs idling while threats slip by; one vivid win: a PowerShell alert cleanup that suppressed 65% of routine noise and let true positives surface immediately (Prophet Security blog on AI detection engineering and incident triage: Prophet Security - AI for detection engineering and triage, Corelight press release on AI Guided Triage: Corelight - Guided Triage press release, CAIFIR certified AI forensics training: Tonex - CAIFIR Certified AI Forensics Incident Responder course).

“Security teams didn't sign up to be human routers.”

OpenEuroLLM puts Czechia squarely in the centre of Europe's push for open, trustworthy foundation models: launched in February 2025 and coordinated from Prague by Jan Hajič at Charles University, the project assembles a 20‑partner consortium to build performant, multilingual open LLMs that can be fine‑tuned for industry and public services while preserving linguistic and cultural diversity; the effort is funded under the EU's Digital Europe Programme and co‑funded by the Czech Ministry of Education, Youth and Sports, signalling a concrete route from national research capacity to deployable public‑sector models (see the Digital Europe Programme overview and the Charles University announcement).

For Czech government teams this means access to transparent model weights, shared evaluation data and EuroHPC compute partners that can speed compliant pilots for chatbots, document processing and policy drafting without starting from a closed black box - a practical boost for NAIS goals and municipal pilots that need explainability and local language competence.

The Institute of Formal and Applied Linguistics brings deep NLP expertise and a 30‑year track record, so OpenEuroLLM is both a technical and institutional bridge between Czech research and Europe‑wide public‑service deployments.

“The transparent and compliant open-source models will democratize access to high-quality AI technologies and strengthen the ability of European companies to compete on a global market and public organizations to produce impactful public services.”

Start small, stay practical and make prompts part of the launch checklist: pick one low‑risk pilot (an action page or a TWIST‑funded chatbot), gather source materials and then craft prompts using the four Gemini prompt areas - Persona, Task, Context and Format - to give the model a clear role and output shape (Gemini prompting guide - Writing effective prompts); for citizen‑facing pages follow the Dept of Civic Things action‑page template (grade‑5 readability, “Need to know”, “Before you start”, “Steps”, “What's next”, “Get help”) and always review AI drafts with subject‑matter experts before publishing (Dept of Civic Things - How to use AI to write content for a government service).

Align each pilot with NAIS priorities - data quality, sandboxes and explainability - and record audit logs and simple explainability notes so results are defensible.

For teams that need hands‑on skills, the AI Essentials for Work bootcamp teaches practical prompt writing and workplace AI use cases and is a direct next step for municipal or ministry staff (Nucamp AI Essentials for Work bootcamp); with a short pilot, clear templates and training, Czech public bodies can turn strategy into reliable, auditable services without waiting for perfect models.