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

Midland legal teams face a moment of practical urgency in 2025: local government investment and statewide rules mean AI prompting is no longer optional - it's a skill that must be precise, ethical, and verifiable.

The City of Midland's proposed technology fund and ITSD expansion signal growing local AI use, while industry data shows AI already supports heavy contract work and can save attorneys 1–5 hours per week - up to 260 hours a year, roughly 32.5 working days - when prompts are well-crafted (Callidus AI legal workflow statistics and prompt effectiveness).

Texas Opinion 705 reinforces that lawyers must maintain technological competence, protect confidentiality, and verify AI output before filing (Texas Opinion 705 guidance on generative AI for attorneys).

For Midland in-house counsel and local firms, a practical path is skills-first: a focused course like Nucamp's 15-week AI Essentials for Work teaches prompt techniques, tool selection, and compliance steps that turn time savings into reliable client service (AI Essentials for Work syllabus (Nucamp)).

“leverage and expand the use of technology to facilitate the exchange of information,”

Prompts were chosen and vetted with Texas-specific ethics, risk, and usability criteria: first, each prompt had to align with Opinion 705's mandates for technological competence, client confidentiality, and human verification (Texas Opinion 705: technological competence and client confidentiality); second, prompts were screened against the new Texas AI Act's prohibitions and documentation expectations to avoid prohibited practices or risky data handling (Overview of the Texas AI Act: prohibitions and documentation requirements); third, the ABCDE prompt framework and prompt-chaining techniques from practitioner guides were used to ensure clarity, jurisdictional context, and repeatability (ContractPodAi guide to AI prompts for legal professionals).

Testing combined red‑team reviews, CLE-style peer review, and simulated workflows that require no client-identifying inputs and an explicit verification step mirroring recent Texas court AI-certification practices - so each prompt is immediately usable for Midland teams while keeping filings defensible in Texas courts.

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

Midland energy contracts demand a redline prompt that targets Texas‑specific risks: ensure parties and signatories are correct, isolate term/renewal language (short‑ vs.

long‑term tradeoffs), flag pricing structure and consumption‑change clauses, surface early‑termination penalties, and call out indemnities, dispute‑resolution/jurisdiction, and any ambiguous attachments or incorporated exhibits; the goal is a machine‑assisted redline that highlights the exact clause, explains the business consequence, and proposes narrow replacement language based on the Texas Contract Review Checklist - jm.legal framework.

Energy‑specific checks - contract length alignment, add/remove‑site flexibility, and how market conditions affect price - are essential because a hidden early‑termination or load‑change trigger can erase expected savings or force expensive renegotiation (NRG: 10 Things to Consider in Energy Contracts); require the prompt to return a short risk score for each redlined item so Midland counsel can prioritize negotiation time and preserve budget certainty.

Build a litigation‑research prompt that pulls and contrasts controlling authority for internet‑activity personal‑jurisdiction issues, prioritizing the Ninth Circuit's Briskin v.

Shopify en banc view that interactive platforms can be “expressly aimed” at forum residents, the Fifth Circuit's recent revival of the fiduciary‑shield doctrine that can protect nonresident officers in Texas‑forum suits, and the U.S. Supreme Court's Fuld decision holding that Fifth Amendment due‑process analysis can diverge when jurisdiction is authorized by federal statute; the prompt should (1) fetch on‑point opinions and short circuit splits, (2) flag whether the claim rests on platform data/commerce, statutory nationwide service, or officer‑level contacts, and (3) produce a one‑line “risk takeaway” for Midland counsel (e.g., probable forum exposure, viable fiduciary‑shield defense, or need for statutory‑jurisdiction briefing) so triage directs resources where litigation costs will concentrate.

This makes clear so what: Texas lawyers must treat platform‑based claims as potential multi‑forum exposure while still checking Fifth Circuit defenses that may keep officers out of Texas courts - exactly the mix that drives early forum‑strategy and preservation work.

See coverage of the Ninth Circuit shift in Briskin (Ninth Circuit Briskin v. Shopify en banc decision (May 2025)), the Fifth Circuit fiduciary‑shield revival (Fifth Circuit fiduciary‑shield revival advisory (Feb 2025)), and the Supreme Court's Fuld guidance on statute‑based jurisdiction (Supreme Court Fuld ruling on statute‑based jurisdiction (June 2025)).

"The Ninth Circuit concluded that it could exercise specific personal jurisdiction over Shopify, an e-commerce company."

Midland SaaS vendors and in‑house counsel should treat CCPA/CPRA readiness as a jurisdictional filter: if a Texas company “does business in California” or touches data for >100,000 California consumers, >$25M revenue, or earns ≥50% from selling/shared data, the CPRA's expanded rights (correction, deletion, opt‑out of sale/share and limits on sensitive personal information) apply even when the business is based in Midland - so start by running the thresholds in a formal checklist (CPRA compliance checklist from IT Governance for California privacy readiness).

A useful AI prompt for counsel asks for (1) a tailored data inventory and flow map, (2) a vendor‑classification matrix that flags service‑provider vs. third‑party contract language, (3) DSAR intake and identity‑verification scripts with a 45‑day response timeline, (4) SPI limitation controls and a “Do Not Sell or Share My Personal Information” footer link, and (5) a prioritized remediation plan with estimated effort and potential fines - because CPRA gaps have produced seven‑figure settlements and regulatory scrutiny and penalties that can reach several thousand dollars per violation.

For practical regs, include CPRA‑specific opt‑out/signal handling and SPI rules from the 2025 compliance playbook when generating the checklist (2025 CPRA compliance checklist and guidance from Transcend).

Build a transactional‑clause generator prompt that outputs three tailored clause drafts (vendor‑first, customer‑friendly, and mutual) and a short negotiation memo: (1) a Limitation of Liability section that defaults to a fees‑based cap (e.g., 12 months' fees) with a clear waiver of consequential damages and specified survival and time‑to‑bring‑claim language, (2) a data‑breach carve‑out that either raises the cap for HIPAA/PII incidents or ties the “super‑cap” to the vendor's cyber‑liability insurance, and (3) BAA/HIPAA boilerplate that lists responsibilities, breach‑notice timelines, and required certifications; also generate an IP assignment/indemnity package that preserves customer ownership of customer data while keeping vendor liability for IP infringement uncapped or separately indemnified.

Require the prompt to flag inconsistent cross‑references (e.g., indemnity excluded from cap but not reflected in the indemnity clause), suggest dollar or fee‑multiple cap options, and produce a one‑line “risk takeaway” for each clause so Midland counsel can triage negotiation leverage quickly.

Use clause samples and negotiation playbooks as fallbacks and demand a short justification for each carve‑out tied to insurance or regulatory risk to avoid surprises at signing (Negotiating SaaS Agreements: liability, data, HIPAA/BAA guidance) and follow market elements - caps, carve‑outs, carve‑in exceptions, and governing law - when proposing language (Limitation of Liability guide and clause templates).

Liability clauses are there to stop loss… stopping your losses is just as important as making more money.

Create a Legal Ops intake + prioritization prompt that mirrors a centralized “intake/assessment” model like 2-1-1 Texas Central Intake and Assessment model, asking for clear structured fields (matter type, critical deadline, named regulators, client data sensitivity, commercial value, and immediate containment steps), plus automatic triage rules that elevate regulatory flags - TABC licensing issues or Permian‑Basin energy projects (e.g., Oxy's Permian DAC work) - and route them to the right queue (contracts, privacy/HIPAA, litigation, or city counsel).

Include built‑in after‑hours lead capture and screening suggestions (keep calls and intake warm with Smith.ai after-hours intake and screening for legal teams), require a one‑line “risk takeaway” and a numeric priority score, and demand evidence fields for verification (attachments, vendor names, license numbers) so Midland in‑house teams can instantly see whether a matter is routine, compliance‑sensitive, or time‑critical - so what: urgent regulatory or data incidents get routed to counsel without delay, reducing the chance that a missed intake becomes an avoidable enforcement or breach escalation.

Finish strong by converting the playbook into two concrete systems: a shared, searchable prompt library and a parallel secure‑workflow checklist that keeps Midland matters out of model training and preserves privilege.

Organize prompts by task (intake, redline, research, privacy), give only a few editors edit rights, and require “no client PII” test runs and scheduled reviews - steps recommended in the TeamAI guide to building an AI prompt library for business (TeamAI guide to building an AI prompt library for business).

Lock down operational controls drawn from in‑house practice guidance - use temporary chats, MFA, and deletion schedules to avoid inadvertent retention - exactly the confidentiality safeguards Sterling Miller outlines for in‑house lawyers in his practical generative AI prompts guide (Practical generative AI prompts for in-house lawyers by Sterling Miller).

Standardize templates and editable prompt‑templates in a managed prompt library (model selection, temperature, JSON/text output) per Microsoft's AI Builder prompt library documentation so Midland teams can reuse audited prompts and trace which model/version produced each result (Microsoft AI Builder prompt library documentation).

The payoff: faster triage, repeatable redlines, and defensible human verification aligned with Texas practice requirements.

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