Work Smarter, Not Harder: Top 5 AI Prompts Every Legal Professional in Denmark Should Use in 2025

For Danish legal professionals in 2025, the message is clear: generative AI is already reshaping workflows but firm-level adoption lags behind individual use, so mastering targeted prompts is the fastest way to win time and reduce repetitive work - global data shows many users save 1–5 hours per week and personal AI use outpaces firm adoption.

Read the Legal Industry Report 2025 for adoption details and the Thomson Reuters roadmap for strategy lessons, then protect clients by using an AI vendor contract checklist tailored to GDPR and EU rules.

Start with a small set of high-impact prompts - contract redlines, client-ready summaries, and focused legal research - to capture efficiency without ceding control, because firms with a clear AI strategy are much likelier to convert tools into measurable ROI while managing ethical and data risks.

“There is a stark competitive divide amongst law firms when it comes to AI, and those without a plan for AI adoption, which is nearly one-third, put themselves at risk of falling behind as competitors transform their operations.”

Selection began by mapping everyday Danish workflows against the changing legal landscape - from the new Danish AI bill and DDPA guidance to EU-level rules - using the detailed jurisdictional snapshot in Chambers' Artificial Intelligence 2025 – Denmark to prioritise prompts that reduce regulatory and data‑risk exposure while delivering measurable time savings; prompts that support contract drafting/review, client-ready summaries, targeted legal research, and compliance checks scored highest because they appear most frequently in practice and tie directly to efficiency benchmarks in industry guides like Callidus AI's prompt playbook (where clear prompting yields 1–5 hours saved per week, or roughly 260 hours a year).

Prompts were then stress‑tested for Danish-specific constraints: GDPR/Article 22 implications, DDPA expectations, and the copyright/documentation risks set out in Gorrissen Federspiel's trade‑marks & copyright review - so each template demands provenance and citation fields that let lawyers retain control.

Final selection favoured clarity, reproducibility, and auditability (so a partner can point to a saved prompt and a clear chain of edits), iterating prompts until outputs were jurisdiction‑accurate, citation-ready and easily reviewed by counsel.

“the party seeking protection must be able to document how the AI tool was used.”

For contract drafting in Denmark, think of a clause library plus a “spellbook” prompt template as the practical heart of faster, safer drafting: a searchable, CLM‑backed repository of pre‑approved clauses (confidentiality, payment, liability, IP) that lawyers and business teams can snap together like Lego pieces to assemble a compliant first draft in minutes; see Swiftwater's primer on clause libraries for the fundamentals and HyperStart's deep dive on business benefits and CLM integration.

The spellbook prompt template captures the variables a Danish lawyer must supply (governing law, GDPR/data‑transfer rules, liability caps, citation provenance) and primes an LLM for jurisdiction‑accurate drafts - follow Laura Greenberg's priming-and-prompting approach to get reliable limitation‑of‑liability language and iterate safely.

Build metadata and citation fields into every clause, enforce role‑based approvals, and require a human redline step so outputs are audit‑ready and defensible under Danish DDPA/GDPR constraints; the result is predictable, auditable drafting that saves time without ceding control.

“Over the past five or so years, one of the key responsibilities businesses are placing on in-house lawyers is spotting and managing risk.”

Contract review in Denmark becomes far more manageable when a Redline + Risk Register prompt forces the AI to behave like a disciplined deputy: compare every edited clause to the firm's approved language, produce a clause-by-clause risk list with a clear risk barometer, and propose concrete mitigations and redlines that a lawyer can accept, modify or revert - exactly the workflow described in Conga's Redline AI documentation for real‑time clause comparison and mitigation guidance (Conga Redline AI documentation for clause comparison and mitigation guidance).

Build prompts that demand provenance (show the standard clause it was measured against), jurisdiction notes for GDPR/DDPA checks, and a short human‑readable justification for each suggested edit so auditors can trace decisions; playbook‑driven redlining delivers the consistency and cycle‑time savings vendors report, from guided redlines to portfolio risk scoring (Sirion AI playbook analysis on redlining vs manual contract review).

For Danish practice, require the prompt to output an “Items at Risk” register, negotiation fallback language, and a publishable audit trail - think of the risk barometer as a surgeon's lamp that instantly illuminates the single clause most likely to cost the firm time or money.

For vendor controls and data‑processing checks, link redline outputs to your AI vendor checklist (AI vendor contract checklist for GDPR and vendor controls).

For Danish matters, a matter‑intake → client‑ready summary prompt should read like a checklist: assign the AI a role (“You are an expert Danish commercial litigator”), provide the facts and documents (redacting names or using generic placeholders per Sterling Miller's guidance), specify jurisdiction (Denmark + DDPA/GDPR checks), and demand a tight format - an executive one‑paragraph summary, key dates, central legal issues, likely outcomes, a short recommended next step, and a provenance field with citations and links; asking for bullet points and a plain‑English client section produces a publishable one‑page brief lawyers can hand a CFO or client without heavy editing.

Insist the prompt require source citations and a short rationale for risk items (so output is audit‑ready), and include a privacy reminder or “do not store” flag when feeding confidential files to public models.

Practical templates and prompt tips - like Clio's research‑and‑summarize examples and Callidus AI's guidance on being specific and direct - show that precise prompts boost usefulness and can unlock the 1–5 hours per week efficiency gains other firms report; keep the prompt reusable by parameterising governing law, contract type, and desired audience so every intake yields a defensible, client‑facing summary ready for review.

Artificial intelligence will not replace lawyers, but lawyers who know how to use it properly will replace those who don't.

A well‑crafted targeted‑research prompt for Danish practice should ask the model to act as a DDPA‑and‑EU‑law specialist, prioritise primary sources (the 26 February 2025 Danish AI bill that would supplement the EU AI Regulation and its potential 2 August 2025 entry‑into‑force), pull relevant DDPA guidance and sectoral notes, and surface controlling CJEU privacy precedents - most importantly the transparency requirements in the Dun & Bradstreet line of cases - so every answer arrives citation‑stamped and risk‑scored for GDPR Article 22 and automated‑decision transparency; see Chambers' Artificial Intelligence 2025 - Denmark practice guide and Kennedys 2025 CJEU data protection case round‑up on transparency and fines for concrete rulings to cite.

Require the prompt to (a) list sources with paragraph‑level citations, (b) flag any conflicts between DDPA guidance and EU AI Act obligations, (c) produce a one‑line “so what” risk verdict for client briefings, and (d) output a short negotiation play (fallback language or DPIA triggers); the result should read like a red‑tabbed case file that instantly shows which precedent or regulation will decide the next step.

Turn the proofread & compliance check prompt into a checklist‑driven gatekeeper: instruct the model to validate GDPR/Danish Data Protection Act touchpoints (legal basis, data‑minimisation, retention, Article 32 security measures, DPO triggers and the 72‑hour breach‑notification rule), output an Article‑28‑ready DPA draft with sub‑processor and audit clauses, run a DPIA trigger test for any high‑risk or profiling use, and produce an

IP & ownership clause

that ties model‑outputs to contract terms and provenance fields so authorship and rights are clear; for AI‑specific risks require an algorithmic‑bias review that flags disparate impacts and suggests human‑oversight controls and mitigation steps.

Embed source citations and an explicit

do not store / minimisation flag

for confidential inputs so auditors can recreate the decision trail - Linklaters' Denmark data‑protection guide and the Danish regulator's DPIA templates are useful references for required approvals and documentation, while a Data Processing Agreement primer helps shape Article 28 language for vendor contracts (Linklaters Denmark data protection guide, Bird & Bird Denmark AI regulatory tracker, Data Processing Agreement (DPA) checklist and drafting primer).

so what

is simple: a single prompt that fails to require a DPIA or DPA can turn a useful draft into an audit headache, so force the model to prove compliance before any text leaves the review queue.

Next steps for Danish legal teams are practical and measurable: begin with a tightly scoped pilot that standardises 3–5 high‑impact prompts (redlines, summaries, targeted research), build a prompt library and role‑based review workflow, and hardwire compliance gates - DPIA/DPA checks, provenance fields and human sign‑off - to satisfy DDPA/GDPR and EU AI Act expectations; Callidus AI's prompt playbook shows how clear prompts unlock 1–5 hours saved per week (five hours ≈ 32.5 working days a year), and Thomson Reuters' guidance underscores the need for precise, context‑rich prompts and curated prompt libraries.

Measure ROI with simple KPIs: hours saved, reduction in redline cycles, number of DPIAs triggered, and incidents avoided, and tie those to billable‑day or cost‑avoidance metrics before scaling.

Train the team on prompt hygiene and governance - consider Nucamp's AI Essentials for Work bootcamp for hands‑on templates and prompt literacy - and treat prompts as versioned firm IP with an auditable approval trail so every AI output is defensible under regulator scrutiny.

“the party seeking protection must be able to document how the AI tool was used.”