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

Orem legal professionals face a 2025 landscape where AI is less a novelty and more a necessary workflow partner: NetDocuments reports 79% of law firm professionals now use AI and a 315% jump in use year-over-year, and generative, agentic tools are becoming “a new legal assistant” that speeds document review, summaries, and contract risk flags - even extracting renewal dates automatically - so small firms can compete with larger shops without hiring extra staff; see NetDocuments' 2025 Legal Tech Trends for details (NetDocuments 2025 Legal Tech Trends AI-driven legal tech trends report).

At the same time Utah's pioneering AI rules mean firms serving local clients must disclose generative-AI interactions under the Utah AI Policy Act (Utah AI Policy Act overview and compliance requirements) and recent amendments now create compliance duties for licensed professionals (Bloomberg Law analysis on Utah AI regulation for lawyers), so adopting prompt-based workflows in Orem isn't just efficiency - it's risk management and client trust, all achievable with practical training like Nucamp's AI Essentials for Work.

“The future of the legal profession demands that AI sits right inside the workflows, right in the places where people are already working. It's not about bringing your content to AI; it's about bringing AI to your content.” - Ludo Fourrage, Nucamp CEO

Selection for the top five prompts followed a practical, risk-aware playbook: begin with proven prompt craft - Thomson Reuters' Intent + Context + Instruction formula - to ensure each prompt supplies jurisdiction, document type, and critical dates so Utah practitioners get legal-grade context; layer on ContractPodAi's ABCDE framework (Audience, Background, Clear instructions, Detailed parameters, Evaluation) to force useful deliverables and consistent formats; and screen every prompt through ethical and confidentiality filters highlighted in Ten Things and Case Status guidance so nothing asks for unredacted privileged data or runs afoul of local disclosure duties.

Prompts were iteratively refined - tested for clarity, persona assignment (e.g., “act as an employment litigator”), and output constraints (brief memo, bullet summary, red-flag table) - and ranked by three criteria: accuracy of the first-pass output, ease of attorney review, and alignment with Utah-specific compliance needs; the result is a compact library of prompts that behave like a reliable junior associate (always asking for the jurisdiction and dates) while keeping client confidentiality and professional responsibility front and center (Thomson Reuters guide to writing effective legal AI prompts, ContractPodAi guide to mastering AI prompts for legal professionals, Ten Things practical generative AI prompts for in-house lawyers).

When asking an AI to synthesize Utah case law for a quick litigation memo, the prompt should steer the model toward the exact statutory hook and the decisive factual signals: instruct it to pull the holding, cite UT Code § 78B-4-513, note privity and any exceptions, and flag design-vs.-tort framing - for example, Hayes v.

Intermountain GeoEnvironmental Services treats a geotechnical report as a “design” input and affirmed dismissal under the Economic Loss Statute after subsidence tied to failure surfaces roughly sixty‑five feet beneath a Layton home; tell the AI to extract that procedural posture, the court's statutory reasoning, and any narrow fact patterns (e.g., physical damage vs.

purely economic loss) that would change the outcome (Hayes v. Intermountain GeoEnvironmental Services - Utah Supreme Court opinion on Justia).

Also ask for a concise checklist of next steps for research (cases on independent‑duty exceptions, privity rulings) and link the synthesis to Utah's court structure so outputs map to precedential weight (Utah Courts opinions and case law overview); outputs should be a one‑page memo plus a two‑column table of citations and actionable flags so an attorney can scan risk‑relevant issues in under two minutes.

For a precedent-focused prompt that actually works for Orem practitioners, tell the AI to treat precedent like a living map: start with jurisdiction and court level, then run a targeted sweep for any statutory amendments or shifts in judicial selection, venue rules, or ethics standards that could change a case's precedential weight -

state lawmakers “passed bills that made partisan changes to judicial selection, manipulated venue rules, and politicized judicial ethics,”

so the prompt should explicitly ask the model to surface those developments and flag cases vulnerable to such shifts (see the Brennan Center report on 2023 legislative assaults on state courts: Brennan Center report on 2023 legislative assaults on state courts).

Add instructions to return a two-column table of controlling vs. at‑risk authorities, a short rationale for why a decision's weight may have eroded, and a prioritized checklist of follow-up research steps; pair this with implementation guidance from Nucamp's AI Essentials for Work syllabus and enrollment resources so firms can operationalize the prompt into daily workflows without losing sight of compliance and client risk: Nucamp AI Essentials for Work syllabus and Register for Nucamp AI Essentials for Work bootcamp - the end result is an “automated courthouse radar” that flags when a precedent's anchor has shifted so attorneys can decide quickly whether to rely, distinguish, or re-research.

For transactional work in Orem, a contract‑review prompt should turn an AI into a focused clause detective: instruct it to pull parties, scope of work, payment schedules, termination and automatic‑renewal language, liability caps and indemnities, IP, blank fields, and any jurisdictional or regulatory obligations (flagging location‑specific risks such as Utah‑specific terms), then output a short red‑flag list, a two‑column “clause → risk” table, suggested plain‑language replacement language, and calendar‑ready key dates for renewals and opt‑outs - this mirrors best practices from contract checklists and risk frameworks and keeps the attorney in the loop for human verification (see HyperStart's contract review checklist and PromptAdvance's ChatGPT prompts for contract review).

Emphasize common high‑impact traps - ambiguous “reasonable efforts,” unilateral amendment rights, and an overlooked auto‑renewal or blank field that can lock a client into another year - and require the AI to produce negotiation fallback positions and a final recommendation (accept, amend, or escalate).

Pair the prompt with a CLM or risk checklist to automate alerts and ongoing screening so early identification becomes routine rather than an emergency after a missed deadline.

“We looked at our average contract value and came up with a specific monetary threshold, below which we'll generally not have a full legal review. However, if it's a business-critical service, or if a lot of personal data (or sensitive data) is involved, then regardless of cost, legal would need to be involved.”

Advanced case evaluation prompts should turn an AI into a seasoned Utah litigator's thought partner: instruct the model to assume the role of an experienced trial attorney, specify jurisdiction and court level, provide a concise factual timeline, list the claim elements and burdens of proof, and then map strengths, weaknesses, and likely outcomes with confidence bands and a prioritized to‑do list (motions, discovery targets, experts to retain, and settlement levers).

Ask for a one‑page strategy memo plus a two‑column “issue → recommended step” table, calendar‑ready deadlines, and a short script for client conversations in plain language so counsel can act fast in Orem's client‑facing context; require the AI to cite sources and flag any gaps in evidence or law that require human verification.

Pair this with tool guidance - use lawyer‑focused prompt patterns and ask the model to propose follow‑ups if key facts are missing - so the firm converts one smart prompt into a repeatable workflow (and, as industry surveys show, saving even five hours a week can add up to roughly 32 full working days a year in reclaimed time).

For templates and example wording, see Callidus AI's advanced case evaluation prompts and Clio's practitioner prompts for legal strategy and research (Callidus AI advanced case evaluation prompts for lawyers, Clio's ChatGPT prompts and legal strategy examples).

Prompt: I want you to act as a legal professional for a civil case. My client, a tenant, is attempting to get their damage deposit back from their former landlord, who kept the deposit despite my client leaving the property in pristine condition. If my client loses this case, please tell me what the potential consequences are for their business or personal life. Please be as detailed as possible, including financial, personal, and reputational consequences.

Design a client-facing prompt that forces plain English: tell the model to translate fee clauses into a two-line “what you'll pay” snapshot, turn dense contract language into a one‑page bulleted memo with a short “what this means for you” paragraph, flag any ambiguous billing terms, and generate calendar‑ready dates and a simple script for the first client call so intake becomes less an interrogation and more a conversation; prompt patterns should borrow the Filevine playbook on explaining fees in plain language (use charts, analogies, and client portals for transparency) and Thomson Reuters' plain‑language principles - identify the audience, use short sentences and active voice, and lead with the main point - so outputs are both accurate and empathetic (Filevine plain-language fee explanations and rapport, Thomson Reuters plain-language principles for legal communication).

For Orem firms, include a prompt step that creates client-facing consent language and a portal-ready summary tied to local intake workflows - pair this with Nucamp's implementation guides for Orem practitioners so firms can operationalize the output without losing compliance or client trust (Nucamp AI Essentials for Work implementation guide for Orem practitioners).

The goal: fewer confused clients, fewer billing disputes, and a moment of clarity so striking it's remembered - like turning a paragraph of legalese into a single grocery‑receipt line that everyone actually understands.

“We should not speak so it is possible for the audience to understand us, but so that it is impossible for them to misunderstand us.”

Wrap AI prompts into everyday practice in Orem with a short, practical checklist: run a targeted pilot (start with contract review or client intake), measure time savings (clear prompts can reclaim roughly 5 hours/week - about 32.5 full working days a year), and lock successful prompts into a governed prompt library using ContractPodAi's ABCDE-style guidance to ensure consistent outputs and ethical safeguards (ContractPodAi guide to AI prompts for legal professionals); pair that with local skill-building - attend a CLE like the University of Utah's “AI and the Future of Legal Practice” to align practice with emerging norms (University of Utah CLE on AI and the Future of Legal Practice) - and operationalize with structured training (consider Nucamp's 15-week AI Essentials for Work to convert templates into repeatable workflows and client-facing outputs: Nucamp AI Essentials for Work bootcamp (15-week AI training for professionals)).

Start small, document review and intake-first, apply prompt governance, train the team, then scale.