Work Smarter, Not Harder: Top 5 AI Prompts Every HR Professional in New York City Should Use in 2025

New York City HR teams must balance big efficiency gains with strict local rules: AI can reduce screening costs by about 75% and cut average time‑to‑hire from 44 days to just 11 days, but NYC requires annual third‑party bias audits, candidate notice and public audit summaries for automated employment decision tools - noncompliance can trigger fines of $500–$1,500 per day - so pilots need audit‑ready documentation and human oversight.

Practical AI wins like 24/7 chatbots and automated short‑listing free recruiters for strategic work, yet governance, bias testing, and vendor controls are mandatory before scaling.

Read the measured benefits and required controls in the AI recruiting research and NYC bias‑audit guidance.

“Using AI in the recruiting process could potentially introduce bias based on the data sets they are trained on,” said Stevens.

Selection prioritized actions that make AI hiring practical and defensible for NYC employers: prompts were chosen to produce audit‑ready artifacts (bias test summaries, data‑source notes, notice text) that map directly to NYC Local Law 144 and its implementing regulations, to embed human‑in‑the‑loop checks and SME validation, and to surface vendor provenance and rollback options so HR can prove traceability and limit legal exposure.

Sources guided a shortlist of five prompt types: legal-compliance prompts that generate public audit summaries and candidate notices (NYC final AEDT regulations for AI hiring), governance prompts that create inventories, DPIA-style risk notes, and human‑review scripts (Baker McKenzie legal playbook for AI in HR), and vendor/audit‑artifact prompts that support annual third‑party bias audits and transparency reporting (LL-144 compliance guide for NYC Local Law 144).

Each prompt was tested for clarity, repeatability, and the ability to produce an evidence trail suitable for audits and hiring managers who must defend decisions.

“We started AdeptID because we see huge potential for AI to identify hidden talent, and help millions find better jobs, faster. HR and Talent applications need more AI, not less. But the AI has to be built and used responsibly - not just once a year but continuously. In Credo AI, we've found kindred spirits who believe AI can be used for good, if it's used responsibly.”

Generate a Workday-ready job description by prompting for two distinct outputs: a concise Job Description Summary (Workday's required field - roughly one paragraph, ~6 sentences) and a fully formatted Job Description broken into Heading‑2 sections with bulleted lists (so it can be pasted straight into the “Create Job Requisition” form).

Follow Ongig's best practices - put the important stuff first, write to the candidate in 1st/2nd person, keep titles short and searchable, and disclose salary/benefits up front - to boost clarity and applicant fit (Ongig guide on writing job descriptions).

Mirror Workday's expected headings (Essential Duties, Minimum Qualifications split into Required vs Preferred, Physical Requirements, Unusual Work Hours, required attachments) and use bullets for duties so hiring managers and HR reviewers see exact evaluation criteria without rework (Workday job-description best practices).

So what: a single prompt that outputs audit‑ready, Workday‑formatted fields eliminates back‑and‑forth edits during requisition submission and ensures minimum qualifications are explicit for fair review.

“I don't believe BlackRock will be ever 100% back in office. I actually believe maybe 60% or 70%…”

Turn resume screening into an audit‑ready, repeatable step by using Lattice's prompt patterns to generate three deliverables for every candidate: a concise resume summary with top technical and soft skills, a job‑match report (match percentage and explicit skill gaps) and a short explainability note or bias‑check checklist for human review - outputs cataloged by Lattice in its article about AI prompts for HR hiring and DEIB (Lattice AI prompts for HR hiring and DEIB).

Pair those outputs with practical prompt examples that extract the top 10 technical and soft skills and compare resumes to job postings (sample resume‑screening prompts and examples for HR practitioners, including prompt templates) (Resume‑screening prompt examples with templates and tips), and build in the NYC disclosure and audit steps called out by recruiting guides (notice language and third‑party bias audits) so each shortlisted candidate has a documented match score, identified gaps, and an attached human‑review note ready for reporting (guidance on ChatGPT for recruiting, disclosure, and bias‑audit steps) (ChatGPT for recruiting: NYC disclosure and bias‑audit guidance).

So what: having the match percentage, gap list, and reviewer sign‑off saved per candidate turns screening from guesswork into a defensible, traceable audit artifact for NYC employers.

Use a single “HireVue behavioral interview generator” prompt to produce an audit‑ready video interview package: five STAR‑framed behavioral questions (with suggested follow‑ups), candidate prep copy (30 seconds to prepare, 60–90 seconds per answer and camera/lighting tips), and an interviewer scoring matrix (1–5 scale with behavioral anchors tied to company values) so each recorded reply maps to explicit competence criteria - a structure shown to reduce bias and enable fair comparisons (behavioral interview scoring matrix guide (Polymer)).

Include a short explainability note for human reviewers that flags unusual nonverbal/voice signals and keyword matches (HireVue formats commonly run 3–7 questions and the platform recommends STAR organization for clear, camera‑ready stories), which helps preserve candidate context when AI scoring is used (HireVue interview format and AI scoring overview (HiveMind), STAR method for video interviews guide (HireVue)).

So what: one prompt that outputs questions, a calibrated rubric, and candidate prep turns every HireVue stage into a repeatable, defensible scorecard hiring teams can archive for audits and calibration.

Turn BambooHR into a repeatable 30/60/90 new‑hire plan generator that outputs audit‑ready artifacts for NYC HR teams: prompt for three phase templates (30‑day learning tasks and account access, 60‑day contribution goals with project assignments and mentor check‑ins, 90‑day autonomy targets with a formal review), SMART goals for each phase, measurable success metrics, scheduled manager check‑ins and a reviewer sign‑off field so every hire has a documented path and an evidence trail for audits; use the Whale 30‑60‑90 onboarding plan template as the goals-and-metrics baseline (including mentor assignment and check‑ins) and Asana 30‑60‑90 day plan examples for phrasing and pacing when prompting the generator (Whale 30-60-90 onboarding plan template, Asana 30-60-90 day plan examples).

So what: a BambooHR prompt that auto‑produces phase objectives, explicit success metrics, and scheduled 1:1s converts onboarding from a checklist into a measurable three‑month plan - employees with effective 30/60/90 onboarding report dramatically higher commitment, making this both a retention and compliance tool for NYC employers.

Use Microsoft 365 Copilot to run surveys and convert responses into audit‑ready insights: Copilot Chat can build a Microsoft Forms survey quickly (

Create a form flow

or the AI Survey Agent to draft questions, distribute, and surface real‑time summaries), while the Viva Glint Copilot Impact Survey supplies a vetted set of core items (productivity, speed, effort, quality) and recommends repeating the survey to track trends and sentiment over time - ideal for NYC employers monitoring adoption and DEIB signals.

Feed survey responses into the Microsoft 365 Copilot impact Power BI template to quantify adoption across Meetings, Email, Teams chat, Documents and Copilot Chat, compare Copilot vs non‑users, and surface “Copilot assisted hours” that can be converted to dollar value using the built‑in hours/value calculator (

$72/hr default

).

So what: combining Copilot Chat survey creation, Glint's fixed impact items, and the Copilot impact report produces a repeatable stream of metrics and a dollarized time‑savings figure NYC HR can include in audits, budget requests, and LL‑144 transparency artifacts.

Start small: pilot a single role, document every tool, and require a DPIA plus human‑in‑the‑loop signoffs before expanding - this converts experimentation into audit‑ready practice that meets New York City expectations for annual bias audits and public summaries.

Build an AI inventory, run a bias check, and draft the candidate notice and bias‑audit summary during the pilot so vendor provenance, data minimization decisions, and reviewer sign‑offs are already in place; these are the exact governance steps legal advisors recommend to limit exposure and preserve hiring fairness (Baker McKenzie legal playbook for AI in HR).

Pair governance with measured ROI: capture time‑saved metrics from each pilot and use them to justify controlled scaling while keeping continuous audits and retraining routines.

Invest in practical upskilling (prompt design, tool oversight) so HR teams retain control - consider enrolling in the AI Essentials for Work bootcamp to gain practical workplace AI skills and prompt-writing training (AI Essentials for Work bootcamp registration) - and treat governance as part of product development, not an afterthought (AI governance best practices for recruiting).

Governance enables responsible, sustainable innovation.