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

For Dutch government beginners, learning to write clear AI prompts and map practical use cases matters because policy, practice and public trust are converging: the Netherlands is recognised for ethical AI work in a landmark Netherlands UNESCO ethical AI report, while TNO's quickscan shows government AI use jumped from 75 to 266 applications (with about 39% deployed by municipalities), driven by needs like knowledge processing, anonymisation and a push for transparency via the national Dutch national Algorithm Register for transparency.

That mix - growing deployment, tighter rules, and public scrutiny - makes practical prompting skills essential for drafting accessible policy briefs, spotting risks, and producing citizen‑facing explanations; short, well‑crafted prompts turn technical outputs into usable, accountable decisions.

To build those hands‑on skills, consider a focused course such as the AI Essentials for Work syllabus (15-week bootcamp), which teaches prompts and workplace applications in 15 weeks.

Program	Length	Cost (early bird)	Link
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work (15 weeks)

Thanks to the Algorithm Register, where governments publish information about their algorithms, anyone can see what is happening.

This selection combined legal, technical and real‑world filters tailored to the Netherlands: every prompt or use case had to map to EU/ national obligations (GDPR, the EU AI Act and Dutch oversight noted in the Chambers Netherlands guide), demonstrate a practicable impact assessment pathway (alignment with the Dutch FRAIA/Future FRIA guidance and DPIA practice), and show lessons from live government pilots - including Amsterdam's Smart Check, which ran about 1,600 applications and cost an estimated €500,000 before the pilot was stopped - so that recommendations are grounded in what actually works or fails in Dutch municipal practice.

Priority was given to deployer roles covered by Article 29a/FRIA templates, cases with clear oversight vectors (DPA, DNB, ACM), and technically feasible architectures (hybrid/on‑premise options and sovereignty-minded stacks) that the Generative AI integration strategy highlights as high‑value and low‑risk to implement.

The result: ten prompts and use cases chosen for legal safety, operational readiness, measurable benefit, and direct applicability to Dutch public servants who must balance rights, transparency and efficiency in practice (sources: Chambers Netherlands, the FRAIA/FRIAA discussion, and Dutch pilot reporting).

Selection Criterion	Representative Source
Legal & regulatory fit (AI Act, GDPR)	Chambers AI 2025 Netherlands practice guide on artificial intelligence
FRIA/DPIA methodology	Guidance on Fundamental Rights Impact Assessments (FRIA) under the EU AI Act
Real-world pilots & lessons	MIT Technology Review report on Amsterdam's Smart Check pilot

“We are being seduced by technological solutions for the wrong problems,”

For a one‑page ministerial brief, the prompt should ask an LLM to extract the bottom‑line decision, headline risks, and three recommended next steps tailored to Dutch governance realities - flag whether the proposal requires Prime Minister attention at the weekly Council of Ministers (briefings commonly happen on Fridays), note that the Prime Minister's Office has limited capacity to evaluate line‑ministry content unless it conflicts with the regeerakkoord, and call out vertical and local funding impacts so municipalities aren't blindsided by cost shifts; link the brief to likely communications needs by naming the Government Information Service/DPC contacts who would handle public messaging.

Useful items to include: a one‑sentence policy summary, immediate legal or political vulnerabilities (coordination gaps, coalition sensitivity), required interministerial actors and sub‑councils, budgetary implications for local governments, and a 48‑hour decision checklist (who signs off, what stakeholder consultations are needed, and whether the issue should be escalated to the PM).

Make the output short, scannable, and stamped “Ready for Council” so a minister can absorb the “so what?” in a single glance - imagine the minister reading it on the tram before the Friday meeting and knowing exactly what to ask next.

For source context see the SGI coordination notes and the Ministry of General Affairs organisation guidance.

Source	Why useful
SGI 2024 Netherlands Coordination report (SGI Network)	Explains PMO role, weekly Council of Ministers timing, and coordination weaknesses
Ministry of General Affairs organisation page (Government.nl)	Details the Office of the Prime Minister, DPC and communications structures to cite in briefs

For a DPIA‑style prompt aimed at Dutch public servants, ask the LLM to run a rapid screening against the nine criteria that make a DPIA mandatory in the EU (flag “DPIA required” if two or more apply), then produce a structured report with: a concise project overview, a full description of the AI tool and datasets, a necessity & proportionality test, a mapped risk register (including profiling/automated decisions, large‑scale or sensitive data, vulnerable groups), concrete mitigating measures, DPO advice, stakeholder consultation notes, and a monitoring/review plan - and if high residual risk remains, recommend prior consultation with the Autoriteit Persoonsgegevens.

Anchor the prompt to Dutch practice by requiring evidence of public‑sector DPO involvement (public administrations must have a DPO) and by citing template checkpoints from practical sources so the output is immediately usable in procurement or governance meetings.

For readers unsure how to structure inputs, point the model to the official triggers and requirements on the Dutch guidance site and to tested templates that convert legal steps into answerable sections so the LLM's output can be dropped into a DPIA record without a rewrite; imagine a welfare‑scoring app that would automatically cut access to services - that single “so what?” decision often pushes a project from optional notes to a full DPIA and possible AP consultation.

Resource	Use in a DPIA prompt
Dutch DPIA guidance - business.gov.nl	Legal triggers, nine criteria, DPO/AP requirements
ICT Institute GDPR DPIA free template - step-by-step checklist	Step‑by‑step template and mandatory elements checklist
IAPP d.pia.lab comprehensive DPIA template for AI	11‑step best practice template for AI, stakeholder framing and report structure

Craft a Public FAQ prompt that tells an LLM to produce short, plain‑language Q&As explaining: what the algorithm is for (purpose and expected outcomes); which datasets it uses and how long they're kept; the legal basis and whether a DPIA or prior consultation was done; practical steps citizens can take if they disagree; and where to check or contest decisions - all tailored to Dutch practice and tone.

Ask for a clear “What this means for you” box, a one‑sentence explanation suitable for publication in the Algorithm Register, and a two‑line “how to appeal” answer pointing to contacts and objection routes; link entries to the national Netherlands Algorithm Register public portal so entries are discoverable and feedbackable (see the Netherlands Algorithm Register and its open development), reference the Dutch government overview of fundamental rights and public values, and cite the Autoriteit Persoonsgegevens guidance on transparency, DPIAs, and privacy-by-design so the FAQ spells out rights under the GDPR. Include a simple example (e.g., a benefits‑screening tool) showing how an outcome could be explained in three steps, and insist the tone be concise enough that a commuter can read it on a tram and immediately know whether to ask for a human review.

"[The Commission] will establish a system for registering stand-alone high-risk AI applications in a public EU-wide database."

Turn parliamentary Q&A into reliable, evidence-backed answers by building a small Retrieval-Augmented-Generation chain: index Dutch parliamentary records, ministerial briefs and policy pages, split documents into ~1,000-character chunks, embed them into a FAISS vector store, and let an LLM synthesise concise answers while citing the source passages - the Modal example shows this exact pattern (chunking, FAISS, OpenAI embeddings, and a show sources option) and even exposes a web endpoint and CLI for live queries so ministers or MPs can fetch answers quickly (Modal RAG Q&A example with FAISS and OpenAI embeddings).

For Netherlands use, tie the retriever to local corpora and the national AI policy context so outputs reflect Dutch targets and procurement realities (Netherlands AI policy and 2050 targets); cache indexes to avoid recompute, set temperature low for deterministic replies, and require the chain to say unknown when no evidence exists - a single routed source line can turn a vague answer into a one-sentence, source-anchored reply that a parliamentarian can trust on the tram ride to a committee hearing.

Component	Modal example setting
Chunk size	~1000 characters
Embeddings	OpenAIEmbeddings (FAISS index)
Retriever	Embedding-based, cached global index
LLM	GPT-4o-mini, temperature 0
Interface	Web endpoint + CLI with show_sources flag

show sources

unknown

An ADS rule‑extraction prompt for Dutch public servants should tell an LLM to pull and classify every regulatory nugget that matters for permitting, safety validation, data reporting and cross‑border harmonisation: flag the Experimental Law on Self‑driving Vehicles (enacted to enable driverless and remote‑driver tests, effective 1 July 2019) and the RDW's admittance/exemption regime and checklists for safety evidence, EMC and FMEA, note operational constraints like time‑limited road sections and the typical 3–6 month RDW handling window, and surface tensions between sources (for example, some summaries still state remote operation without a safety driver is not allowed).

The prompt should also extract policy goals - EU harmony, safety first, and benefits such as reduced accidents and platooning fuel savings of 5–15% - so each excerpt is tagged (permit, technical test criteria, data/reporting, liability/insurance, stakeholder actor) and rated by immediacy (apply now / monitor / legislative change).

Ask the model to output machine‑readable citations and a short “so what?” line per rule (e.g., “needs RDW admission → start safety validation now; expect 3–6 months”), making the result a drop‑in checklist for procurement, pilots and DPIAs; see the Dutch government guidance on self-driving vehicle experiments (Government.nl) and the RDW Connected Automated Vehicle guidance and application forms (RDW) for source text and application forms.

Source	Notable regulatory excerpt
Dutch government guidance on self-driving vehicles (Government.nl)	Experimental law enabling permits for public‑road tests, safety demonstration required; RDW issues exemptions.
RDW Connected Automated Vehicle guidance and application forms (RDW)	Tests permitted under conditions since 1 July 2015; practical admittance procedure and applications available.
ConnectedAutomatedDriving.eu country page - Netherlands regulation & policy	Detailed application checklist, required evidence (FMEA, EMC), average handling time 3–6 months; notes on driver/remote‑operation rules.
OECD policy summary - Experimental Law on Self‑driving Vehicles	Summarises 2019 law allowing driverless and remote‑driver experiments under strict conditions and reporting to RDW.

"I want to show you that there are opportunities for us, and that we all have to play a role."

A practical tender specification for a high‑risk AI system in the Netherlands must turn legal obligations into testable requirements: require the supplier to prove EU AI Act compliance by supplying the AI system's EU database registration number and supporting evidence (so evaluators can verify registration in minutes), mandate a DPIA and ongoing post‑market monitoring plan, and embed measurable model clauses on data governance, explainability, human oversight, robustness, cybersecurity and logging so audit trails are auditable and verifiable; guidance on these steps is usefully summarised in a practical procurement guide that ties model clauses to GDPR (practical guide to AI procurement with model clauses and GDPR).

Use the EU's MCC‑AI / model contractual AI clauses as a starting contractual schedule (pick the High‑Risk template and adapt evaluation criteria), and require technical documentation, conformity evidence or third‑party assessment where applicable, plus clear acceptance tests and supplier obligations for bias‑monitoring and incident reporting; the Community of Practice maintains downloadable High‑Risk and Non‑High‑Risk clause templates that should be attached to tender documents (EU model contractual AI clauses for procurements).

The “so what?”: a tender that insists on the registration number, testable acceptance criteria and post‑market checks turns abstract compliance into a procurement‑grade gating mechanism that prevents a risky system from arriving in production unchecked.

Key tender element	Why include it
EU database registration & evidence	Confirms system is listed and discoverable under the AI Act (EIPA guidance)
MCC‑AI / model contractual clauses	Provides standard contractual building blocks for high‑risk obligations and evaluation
Technical documentation, logging, DPIA	Makes conformity, traceability and data‑protection verifiable at award and during operation
Post‑market monitoring & incident reporting	Ensures ongoing compliance, bias detection and corrective action after deployment

When asking an LLM to analyse a set of anonymised citizen‑service chat logs and propose top 10 measures for Dutch public services, frame the prompt so the model first validates the anonymisation method against the GDPR standard (irreversible anonymisation, not mere pseudonymisation) and checks for stylometric re‑identification risks flagged by forensic authorship research; include a pointer to a data‑anonymisation checklist (techniques, subsetting, referential integrity) so the model can report residual PII and the likely utility loss from each technique, and ask for clustered themes, sentiment trends, escalation triggers, repeat contact drivers, and evidence of bias affecting vulnerable groups.

The output should then propose ten concrete measures tailored to NL contexts - examples: improve anonymisation pipelines (masking + consistent referential integrity), introduce targeted subsetting to reduce scope, apply synthetic text redaction for free‑text chats, add stylometry‑risk checks before reuse, record and shorten retention windows, route high‑risk threads to human review, train agents on flagged complaint types, publish a transparency note linked to national AI policy aims, and involve the DPO before any model training on logs - each measure scored for privacy impact and implementation effort so municipal teams can act.

For background on robust de‑identification and pitfalls like the Netflix re‑identification episode, see the Tonic anonymization guide and forensic authorship analysis literature.

Technique	When to use / Risk
Data masking	Good for realistic test data; risk if predictable one‑to‑one mappings remain
Pseudonymization	Use when re‑identification under control is required; not GDPR‑safe anonymisation
Data synthesis	Useful for unstructured chat text; limits re‑identification but may lose referential fidelity
Generalization	Reduce granularity (e.g., age ranges) to lower ID risk while preserving analytics
Data swapping	Obscures record links for statistics; maintain care with correlated fields

How can a researcher be sure to analyse the communication of children and adolescents and not the chat communication of adults who pretend to be under 18?

A readiness prompt for a self‑hosted European LLM should turn strategy into a clear compliance roadmap: ask the model to produce a phased 18‑month plan (pilot → scale → agent‑based), costed estimates, and a checklist linking technical controls (air‑gapped on‑prem options, OWASP LLM mitigations, prompt‑injection defenses) to legal gates (mandatory DPIA triggers under GDPR/AVG, Wet Open Overheid transparency and Archiefwet retention rules), while recommending specific sovereign models such as Mistral Large 2 or Aleph Alpha and GAIA‑X/OVHcloud deployment patterns so procurement teams can verify data‑sovereignty claims quickly; anchor the report to measurable goals (the integration strategy forecasts 150–250% ROI within 24 months).

“so what?” line that flags when to stop and consult the DPO or pause deployment - for example, if sensitive code or personal data enter training pipelines, switch to on‑premise or air‑gapped processing immediately.

For source guidance, see the Generative AI integration strategy for Dutch government and related Dutch AI policy context.

Readiness Element	Key detail from sources
ROI & timeline	150–250% ROI within 24 months; 18‑month phased roadmap
Phase 1 (Months 1–6)	Foundation, basic RAG, €100K–€150K
Phase 2 (Months 4–9)	Scale, on‑premise deployment, €300K–€500K
Phase 3 (Months 10–18)	Agent systems, full CI/CD, €500K–€1M
Model & infra	Mistral Large 2 / Aleph Alpha; GAIA‑X, OVHcloud, hybrid on‑prem
Compliance & security	GDPR/DPIA, Wet Open Overheid, Archiefwet, OWASP LLMs, air‑gapped options

Build an incident‑response playbook prompt that turns legal deadlines and technical forensics into an operational checklist for Dutch public services: tell the model to produce step‑by‑step detection, containment and evidence‑preservation actions (snapshot models, immutable logs, chain‑of‑custody for training data), a rapid impact triage that assesses harm to safety, health and fundamental rights, and a mapped notification flow that names the national CSIRT and competent authority contacts plus escalation to EU coordination bodies where cross‑border effects appear; anchor every step to legal triggers so responders know whether an event is an initial NIS2 “early warning” (report within 24 hours) or an EU AI Act “serious incident” report (72 hours) and whether GDPR breach rules also apply.

Insist the playbook includes forensic checks for data‑poisoning/backdoor patterns, supplier/supply‑chain forensics, short‑term mitigations (rollback, isolation, human‑in‑the‑loop gating), and a mandatory post‑incident update to the DPIA, procurement clauses and monitoring dashboards - because a single poisoned training file can silently embed a backdoor that flips outcomes on a trigger phrase, turning a harmless welfare‑screening app into urgent human‑review work.

For legal context, see the NIS2 Directive requirements and guidance and the EU AI Act incident reporting guidance (Article 73) so the output is immediately actionable for Dutch teams.

Regulation	Initial report window	Notify
NIS2 Directive official page	~24 hours (initial/early warning)	National competent authority / CSIRT
EU AI Act Article 73 reporting guidance	72 hours	Relevant AI/sectoral authority; keep records for follow‑up
GDPR	72 hours (personal data breach)	Supervisory authority / DPO

A practical Monitoring Dashboard & Interpretability prompt for Dutch public servants asks an LLM to output a compact spec that maps live KPIs (performance, data drift, hallucination/safety signals, fairness slices, privacy‑leak alerts, audit logs) to governance actions (DPO consult, rollback, or remedial retraining), a set of interpretability probes (local explanations, counterfactual tests, feature‑attribution and concept‑activation checks) and an automated reporting cadence that feeds into executive‑level views - think one “red/amber/green” line a minister can read on the tram that answers “safe to deploy?” in a glance.

Anchor the spec to standards and tooling: use ISO/IEC 42001 lifecycle controls and AIIA triggers from the AWS guidance, link monitoring and attestations into a ModelOp‑style reporting engine for auditability and regulatory roll‑ups, and specify observability metrics Fiddler recommends (drift, bias, safety, privacy) while warning teams to validate explainers per the World Privacy Forum's tool‑assessment lessons; for Netherlands policy fit, tie outputs to national targets and procurement certainty via local AI policy pages.

The prompt should also produce clear escalation scripts and a developer‑facing checklist so dashboards become governance‑grade, not just pretty charts.

Widget / Probe	Purpose	Source
Performance & Drift	Detect model degradation and trigger retraining	Fiddler AI governance, risk, and compliance solutions, AWS ISO/IEC 42001
Explainability (local & global)	Provide human‑readable reasons for decisions	World Privacy Forum; ModelOp AI governance reporting engine
Privacy & Audit Logs	Evidence for DPIAs, audits and register entries	ModelOp reporting; AWS ISO/IEC 42001
Bias & Fairness Slices	Spot disproportionate harms to groups	Fiddler; World Privacy Forum

“We didn't want to stifle the creativity of our data scientists, both professional and citizen. Our AI Governance software enables us to deliver robust, value-generating models at speed and keep them that way. We aim to monitor hundreds of AI models in production.”

Next steps for Dutch public servants are practical and urgent: treat DPIAs as a first‑order task (a DPIA is mandatory if two or more of the nine legal criteria apply and it's the easiest way to spot privacy risks early), register algorithmic systems in the national Algorithm Register and build vendor checks that demand AI Act evidence (registration numbers, conformity docs) so procurement is a true safety gate; if residual risk remains after mitigation, consult the Autoriteit Persoonsgegevens before you start.

Match your rollout to the AI Act schedule (phased compliance and transparency duties are already in force) and prefer a hybrid, sovereignty‑minded stack for sensitive workloads as recommended in Dutch integration plans - it's a way to capture the projected ROI while keeping data on‑premise when needed.

Operationally: run a short pilot with a clear DPIA + monitoring plan, add human‑in‑the‑loop gates and incident playbooks (one poisoned training file can silently flip a welfare‑screening app into an emergency human‑review problem), and train staff in prompt design and governance - for hands‑on skill building consider the AI Essentials for Work syllabus.

Useful references: the Autoriteit Persoonsgegevens DPIA rules, the government AI Act overview, and practical hybrid deployment guidance for Dutch teams.

Program	Length	Cost (early bird)	Link
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work bootcamp - Register (15 Weeks)

A DPIA is an instrument for identifying the privacy risks of data processing beforehand. You can then take measures to mitigate those risks.