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

Clear, precise prompts are the practical lever that turn Sweden's national AI ambitions into reliable public services: by steering models to produce concise citizen‑facing answers, automate routing and classification, and summarise long reports, prompt design helps realise the priorities laid out in Sweden's national AI strategy report (AI Watch) - education, research, innovation and robust infrastructure - and complements programmes like AI Sweden and DIGG's work on data and public‑sector uptake.

The recent government commission roadmap (AI‑RFS) - which calls for urgent action and even proposes a €1.5bn boost to scale AI capacity - underlines why prompt skills matter now: faster pilots, safer rollouts and better public value from shared data platforms such as the Data Factory.

For civil‑service teams looking to build those prompt-writing muscles, the AI Essentials for Work bootcamp syllabus teaches practical prompt craft and workplace AI workflows to turn strategy into delivery.

“if Sweden can strengthen policy conditions across all policy areas, it will be well placed to offer an internationally attractive working environment for business, researchers and others interested in AI research, development and use.”

The top‑10 prompts and use cases were chosen by mapping concrete public‑sector needs against Sweden's stated AI priorities - education, research, innovation and robust infrastructure - as set out in the national strategy and related guidance, while testing each candidate for technical readiness, data availability and regulatory or human‑rights risk.

Selection steps included cross‑referencing policy guidance from An AI Strategy for Sweden and the European Commission's country review to prioritise use cases that deliver clear citizen value, checking for piloting and testbed fit with AI Sweden and Vinnova resources, and scoring each use case for deployability (data quality via the Data Factory), cost‑benefit and oversight needs.

Special attention was paid to any application that could affect fundamental rights: recent investigative findings about automated risk‑scoring at the welfare agency signalled that high‑impact systems require extra transparency, human‑in‑the‑loop controls and legal review under emerging EU rules.

The result is a practical, risk‑calibrated list of prompts aimed at speeding pilots in areas from classification and summarisation to climate and public‑health analytics while keeping citizen trust front and centre (see the EU review and national strategy for background).

“The entire system is akin to a witch hunt against anyone who is flagged for social benefits fraud investigations.”

Automating classification and routing of incoming e‑mails and documents is a practical first step for Swedish agencies that want immediate wins: RISE's work on a government‑adapted language comprehension model - trained in part on the National Library's texts going back to 1661 - shows how an AI “language brain” can understand context rather than just keywords, so a note about an F‑skattsedel is routed correctly even if the exact term is missing; that makes case handling faster, reduces backlog and lets skilled officers focus on complex decisions instead of triage.

Pilot tooling from the RISE AI in Swedish public sector project and the broader coordination under AI Sweden language models for Swedish authorities targets core tasks like text classification, named‑entity tagging and semantic similarity, and pairs model releases (e.g., GPT‑SW3 families) with evaluation and data‑readiness guidance so agencies can deploy routing and classification with measurable oversight; a vivid payoff is simple yet powerful: fewer misdirected e‑mails, fewer frustrated citizens, and a calmer inbox for every caseworker.

“E-mail, as an example, can be forwarded automatically to the correct departments or correct people or categorised in a certain way.”

Generate concise, citizen‑facing answers for government webpages by using prompt‑tailored models to turn dense policy texts into short FAQs, clear checklists and step‑by‑step guidance that point people to the right next action; the recent Migration Agency changes are a sharp example - an 11‑page supplementary questionnaire sent to roughly 6,500 applicants from a queue of over 86,000, with just a three‑week deadline, makes plain why web copy must be unambiguous and actionable.

Models can be prompted to summarise the new “personal appearance” identity rule, list required documents (passports, residence cards), and explain travel‑and‑work history fields in plain Swedish while linking to official guidance such as the Migration Agency's notice on the changes and reporting from The Local for context.

A single vivid microcopy - “You must visit the Migration Agency in person to prove identity” - can cut confusion; prompts that produce that kind of hard, verifiable sentence (plus a short “what to bring” checklist and the helpline link) make agency front ends more navigable without diluting official requirements.

“it's important that you fill out the forms as correctly as possible. If there are questions or things that aren't clear, the Migration Agency can ask for additional information or need to further investigate the case.”

Long, dense reports need not swamp agency managers: tried-and-tested prompt templates and workflows can turn sprawling policy papers into crisp manager briefings that list the top findings and three clear action items, or compress a 50‑page study into two paragraphs with prioritized next steps - a capability shown in practical templates from PromptLayer's summarisation guide for AI prompts to summarise long reports.

For Swedish agencies this means pairing chunking and accumulative summarisation with iterative refinement so models preserve technical accuracy while surfacing decisions, deadlines and owners; the PromptLayer prompt‑engineering playbook for summarisation explains extractive vs.

abstractive tradeoffs, chunking strategies and multi‑turn prompts that produce both an executive summary and a vetted action list. Metadata and provenance practices like those discussed in the COMET taskforce work also matter for traceability, helping managers trace each action back to the source paragraph or dataset - a small change that can turn a long, intimidating report into a meeting‑ready checklist and spare an overworked official hours of reading.

“The current system for the maintenance and enrichment of PID metadata is inefficient and disconnected.”

When agencies such as the Swedish Tax Agency and Försäkringskassan face mountains of PDFs, scanned letters and varied forms, intelligent document processing (IDP) turns that unstructured mess into clean, actionable data: tools with OCR, pretrained templates and natural‑language triggers can pull names, dates, invoice lines or identity numbers and push them straight into case‑management systems so caseworkers spend time on decisions, not data entry.

Practical approaches include end‑to‑end IDP platforms that ship with out‑of‑the‑box templates and downstream integrations (see MuleSoft's Intelligent Document Processing) and specialised extractors that claim near‑perfect capture rates and massive speedups for invoices and forms (DoxAI's Extract AI reports 99.97% accuracy and 40x faster processing).

For prompt engineers and developers, LLM techniques - from careful prompting to function‑calling and JSON response schemas - ensure the model outputs machine‑readable fields ready for validation and traceability (see Guillaume Laforge's guide on getting LLMs to spit JSON).

The payoff is tangible: instead of queuing thousands of unread letters, an agency can surface the exact sentence that triggered a benefit review and route it automatically - a small change that can turn a backlog into a calm, query‑free inbox for frontline staff.

Detecting intent reliably across Sweden's many regional variants - from skånska to the northern Norrbotten dialect - is a practical barrier to fair, fast digital services, because the same request can be phrased very differently and trigger the wrong workflow; recent work on dialect‑to‑standard normalization shows that mapping phonetic transcriptions to an orthographic norm helps models see through surface variation and recover the user's true intent (dialect‑to‑standard normalization study).

In practice, that means prompts and preprocessing pipelines should include normalization steps so that regional word choices don't become false flags - a small engineering change that cuts misclassifications and reduces needless follow‑ups, improving both citizen experience and caseworker efficiency.

For agencies planning pilots, pairing normalization research with practical roll‑out playbooks keeps projects grounded (see the Complete Guide to Using AI in the Government Industry in Sweden in 2025) and helps ensure that language diversity becomes an asset rather than a blind spot.

Keeping AI outputs safe, unbiased and appropriate in Swedish public services means turning abstract principles into testable audits: RISE's work on “AI and values” stresses identifying mistake rates, mapping which legal values (for example GDPR rights) are being encoded, and choosing boundary‑marking concepts that define what an AI is allowed to decide; this makes audits concrete rather than theoretical.

Practical checks pair automatic metrics (fairness, error‑rate by subgroup, provenance) with human‑in‑the‑loop review, documented explainability and multidisciplinary sign‑off so errors don't cascade into real harm - the national conversation and Sweden national AI strategy for public services both call for coordinated frameworks that link policy, procurement and testbeds.

For prompt teams, that means embedding normalization, traceability and clear acceptance criteria into every pilot, using RISE's labs and policy work to translate values into engineering tests and governance playbooks that catch biases before they reach a citizen-facing decision.

“For example, when diagnosing diseases with the help of AI systems, there is a difficult balance between integrity, precision, and the quality of the prognosis. The more integrity, the more uncertain the AI-based diagnosis, and uncertain AI decisions require more robust explainability. What reduces the precision of AI decisions is the deliberately generated uncertainty required in the forecast, to protect the privacy of the individual in the data set,” Rami continues.

Regulatory and ethical impact assessment is the guardrail that turns promising AI pilots from KOMET or DIGG into trustworthy public services: every proposed use must be checked against the GDPR and Sweden's Data Protection Act, the AI Act's risk categories and practical guidance from IMY so procurement teams can spell out DPIA/FRIA obligations, data‑minimisation and audit rights in contracts rather than discovering gaps later.

Practical playbooks should lean on national implementation notes (see Sweden's overview of data protection law) and the recent reviews of AI‑and‑data interplay that highlight IMY's sandboxes and evolving AI Act rules to align procurement, legal and technical teams early on; treating impact assessment as a procurement deliverable keeps vendors honest and gives agencies concrete mitigation steps instead of abstract assurances.

A vivid test: a procurement that omits a DPIA is like buying a car without brakes - flashy until the first descent - so require traceability, public‑facing summaries and revalidation checkpoints in every contract to protect rights, satisfy auditors and preserve citizen trust (and to make KOMET/DIGG pilots auditable end‑to‑end).

Demand forecasting and resource planning are core tasks for agencies like Arbetsförmedlingen because Cedefop's forecasts show that most employment growth to 2025 will be in non‑marketed (mainly public‑sector) services, with roughly 31% of job opportunities falling to professionals and replacement demand expected to be about ten times larger than expansion demand - a stark signal for staffing strategy.

The PES already combines short‑term projections, the Job Compass / Hitta yrkesprognoser tool and even machine‑learning inputs to map regional needs, while long‑term scenario numbers are set out in Cedefop's Sweden skills forecasts to 2025; pairing these data sources with prompt‑driven AI workflows (scenario generation, vacancy vs.

training slot matching, and regional “what‑if” simulations) turns raw forecasts into concrete actions: where to open vocational courses, how many caseworkers a municipal eldercare group will need, and which occupations to prioritise outreach for.

For planners, the practical win is simple and memorable: use forecasts to add training places or staff weeks before queues appear, not after - see the Cedefop forecasts and the 2023 skills‑anticipation update for the underlying data and methods.

Climate and sustainability decision support is moving from theory to municipality‑ready tools in Sweden: projects funded or coordinated through Vinnova and national centres are combining remote sensing, GIS and machine learning to spot exactly where grey surfaces can become resilient green infrastructure, helping planners balance flood control, biodiversity and recreation in one pass.

RISE's AI‑and‑GIS decision‑support work - tested in Malmö and Uppsala - trains models on spatial and ecosystem data to prioritise sites where converting a parking lot into green space improves flow regulation and cools city blocks, while KTH's AI‑driven spatial‑planning programmes extend that work into carbon‑neutral scenarios and test scalable AI‑DSS tools for Stockholm, Trelleborg and other regions.

These initiatives show a pragmatic “people and planet” payoff: clearer trade‑offs for planners, ready‑to‑use scenario outputs for local politicians, and shared playbooks that prevent 290 municipalities from reinventing the wheel.

Learn more about RISE urban‑green project (AI support for transforming surfaces into multifunctional green spaces - project page) and KTH AI‑DSS for climate‑neutral cities (AI‑Driven Sustainable Spatial Planning - project page) for practical starting points and data models for pilots.

“There is no contradiction in investing in AI and investing in AI for the climate”

Public‑health analytics in Sweden depends on clean, discoverable datasets and that starts with meticulous metadata and sensible sharing workflows: SciLifeLab's Data Platform and its repository give public‑health teams the technical plumbing, templates and programmatic APIs to publish FAIR, traceable datasets so models used for outbreak analysis or vaccine monitoring aren't blind to provenance or variable definitions.

Practical steps include using community metadata standards and ontologies to normalise fields (so organism, sample‑prep and resolution are machine‑readable), creating a metadata‑only record when files must stay restricted, and choosing the right repository for genomics or proteomics outputs; the SciLifeLab submission guidance and reviewer checklists walk agencies through README, manifest and embargo options to avoid the common trap where a missing sample date or undefined variable renders a dataset unusable.

For COVID‑era reporting there's specific advice on where to publish pandemic data via the Swedish Pathogens Portal, while the Data Platform bundles national compute, storage and FAIR support so public‑health analytics can move from ad‑hoc spreadsheets to reproducible, auditable models - one small metadata fix can be the difference between a traced transmission chain and a dead end.

“I found it very useful, as tools such as MetaboLights are becoming increasingly important for advancing metabolomics research and ensuring open, FAIR data sharing. Understanding these resources helps us better support researchers in computational metabolomics.”

Start small, aim for measurable wins, and make governance part of every pilot: Sweden's AI Playbook and the Total AI Governance thinking call for targeted oversight, pilot testbeds and centres of excellence that pair ethical guardrails with real service improvements, and the EU AI Watch guidance for the public sector emphasises the same practical mix of pilots, data quality and skills building - so a sensible first step is a focused prompt pilot (classification, citizen Q&A or report summarisation), an embedded DPIA and an accountable review loop with legal, IT and user representatives.

Training and shared playbooks matter: practical courses that teach prompt craft and workplace workflows turn policy into delivery, which is why hands‑on programmes such as the AI Essentials for Work bootcamp are a natural complement to national initiatives; combine that upskilling with the convening work shown at AI Sweden's Almedalen events and the result is tangible capacity rather than abstract strategy.

Think of one well‑designed prompt as a signpost in a crowded station: it reduces confusion, speeds the right action and builds public trust - then scale what works, keep audits and provenance visible, and feed lessons back into procurement and sandbox rules so Sweden's ethics‑forward ambition becomes everyday practice.

“The question is simply where the money will come from, what is going to pay for welfare in the EU if we continue to fall behind.”