ChatGPT Prompts for HR: 40 Prompts and What Not to Share
Ready-to-use ChatGPT prompts for HR tasks: job descriptions, offer letters, interviews, policies, reviews, plus what you must never paste in.
ChatGPT Prompts for HR
Forty prompts you can copy for the HR tasks a small business actually has: job descriptions, offer and rejection letters, interview questions, onboarding plans, policy drafts, review phrasing, difficult conversations, and surveys, plus the five categories of information that must never go into a general purpose AI tool
The first job description I wrote with an AI tool took four minutes and was useless. It described a person who did not exist, doing a job I had not described, in the voice of a company that was not mine. The second one took twenty minutes, because I spent sixteen of those writing the prompt, and it came back about ninety percent usable.
That ratio has held for every HR task I have handed to a general purpose model since. The output is only as specific as the input. Most pages on this subject give advice about prompting rather than prompts, which is a bit like handing somebody a book about knife skills instead of dinner.
So this is the prompts themselves, forty of them, grouped by the task you actually have in front of you. It also covers the part that gets skipped: what must never go into a general purpose AI tool, and why everything it produces is a first draft. I build HR tooling for teams without an HR department at FirstHR, and the failure I see most is not a weak prompt. It is somebody pasting a whole personnel file into a chat window to save ten minutes. This is general information, not legal advice.
What a Working Prompt Contains
Every prompt that produces something usable contains four things: a role, context, constraints, and a format. Drop any one of them and the output degrades in a predictable way, which is why the prompts below all look structurally similar.
The single highest leverage habit is the negative instruction. Telling a model what not to do closes the gap where it would otherwise supply confident specifics you never gave it: a benefit you do not offer, a notice period you never set, a statute that does not say what the sentence claims.
One more framing point before the prompts. A general purpose model is a writing and structuring tool, not a decision tool. It is good at turning your rough notes into clean language and at spotting what is missing. It is bad at knowing anything about your business that you have not told it.
Job Description Prompts
Use three prompts rather than one: draft, then trim, then check the language. The draft is the least valuable step, because the model is only reorganizing what you gave it. The trimming pass is where a posting gets meaningfully better.
| Task | Prompt to paste |
|---|---|
| First draft from rough notes | You are an experienced HR writer for a US small business with no HR department. Write a job description for a [ROLE] at a [INDUSTRY] company with [N] employees. These are the duties I actually need covered: [PASTE YOUR ROUGH LIST]. Give me six to eight responsibility bullets, a short must-have list, a separate nice-to-have list, and a two sentence company paragraph I will edit. Plain American English. Do not add any requirement I did not give you. |
| Trim inflated requirements | Review this job description and flag every requirement that is not genuinely necessary to do the job well in the first ninety days. For each one, tell me in one sentence whether it is likely to shrink my applicant pool without improving hire quality, and suggest a lower barrier alternative. Do not rewrite the posting yet. [PASTE DESCRIPTION] |
| Check for exclusionary wording | Read this job description and list any wording that could discourage qualified applicants or read as a preference based on age, sex, national origin, disability, religion, or family situation. Quote the exact phrase, explain the problem in one sentence, and give a neutral replacement. Change nothing else. [PASTE DESCRIPTION] |
| Produce a junior or senior version | Rewrite this job description for a more junior version of the same role. Keep the duties that stay the same, change the scope and autonomy language, and then tell me in three bullets what actually changed so I can sanity check it. [PASTE DESCRIPTION] |
| Cut it down for the posting | Turn this job description into a 120 word posting summary plus a one line role headline. Keep [PAY RANGE] and [LOCATION] as placeholders rather than filling them in. Do not add any selling language about culture that is not supported by the text I gave you. |
The exclusionary wording pass is worth running even when you are confident. It routinely catches phrases like recent graduate, young and energetic, or a physical requirement that has nothing to do with the actual work.
Offer and Rejection Letter Prompts
Letters are the safest category for AI drafting because the content is short, the structure is conventional, and you already know every fact that belongs in it. The risk is invention: a model will happily add a term you never agreed to.
One habit worth building: the offer letter is the document most likely to create an obligation you did not intend. Anything a model adds about tenure, promotion, or future compensation should be deleted rather than edited.
Interview Question Prompts
The best use here is not generating questions but building a structure around them: questions tied to real duties, scoring guidance written before you meet anyone, and follow-up probes for rehearsed answers.
| Task | Prompt to paste |
|---|---|
| Build a structured question set | Write eight interview questions for a [ROLE] at a [INDUSTRY] small business. Four behavioral, in the form tell me about a time when. Four situational, in the form what would you do if. Every question must map to a specific duty in this job description, and say which one. For each question give me a three level scoring guide: strong answer, acceptable answer, weak answer. [PASTE DESCRIPTION] |
| Write follow-up probes | For each of these interview questions, write two short follow-up probes I can use when a candidate gives a rehearsed or vague answer. Keep them open rather than leading, and no more than one sentence each. [PASTE QUESTIONS] |
| Flag questions you should not ask | Review this list of interview questions and flag any that touch age, disability, medical history, national origin, religion, marital or family status, pregnancy, arrest record, or citizenship beyond basic work authorization. For each flagged question, give a lawful alternative that gets at the same job related information. [PASTE QUESTIONS] |
| Design a practical exercise | Design a 45 minute practical exercise for a [ROLE] candidate that mirrors real work, can be scored by a non-expert using a rubric, and does not produce work my business would otherwise pay for. Give me the candidate instructions and a five point rubric with observable criteria. |
| Organize interview notes | Here are my notes on three candidates for [ROLE], with names replaced by A, B and C. Organize them into a comparison against these five criteria: [LIST]. Keep my wording wherever it is evaluative, and list what I still have not learned about each person. Do not rank them and do not fill gaps with assumptions. [PASTE NOTES] |
Two things about the last prompt. Replace names with letters before you paste, and delete anything a candidate told you about health, family, or immigration status rather than asking a model to ignore it. Interview notes are the most personal material most small employers generate during hiring.
The legality pass is genuinely useful, but treat it as a prompt to check yourself rather than as an answer.
Onboarding Plan Prompts
Onboarding is the task where a model earns its keep, because the work is mostly structuring: turning a list of things that must happen into a sequence with owners and dates. Give it your real task list and it will do that well.
The remote rewrite prompt is the one I would run first. Checklists written for an office fail quietly rather than loudly: nobody reports that the introductions did not happen, they just do not happen.
Policy Draft Prompts
A model can write policy language. It cannot decide what your policy should be, and it should never be trusted on what the law requires. Describe the practice you already follow and ask it to turn that into a policy, with statutes explicitly off the table.
| Task | Prompt to paste |
|---|---|
| Draft a policy from your actual practice | Write a [TOPIC] policy for a US small business with [N] employees in [STATE]. Base it only on the practice I describe here: [DESCRIBE WHAT YOU ACTUALLY DO TODAY]. Short sentences, name the person responsible, say plainly what an employee should do. Mark anything that varies by state as [CHECK STATE LAW] rather than guessing, and do not cite statutes, regulations, or case law anywhere. |
| Rewrite a policy in plain English | Rewrite this policy at an eighth grade reading level without changing its meaning. Keep every obligation, deadline, and exception exactly as it is. Then list any sentence where you are not certain that simplifying preserved the meaning. [PASTE POLICY] |
| Find the gaps in a handbook | Here is the table of contents of our employee handbook. List the policies a US small business of our size commonly has that are missing here, sorted by how often the gap causes a real problem. Do not draft anything yet, and do not include policies that only apply to large employers. [PASTE CONTENTS] |
| Turn a policy into a manager one-pager | Turn this policy into a one page summary for managers. Cover what they must do, what they must not decide on their own, who to escalate to, and the three questions employees ask most about this topic. Keep it under 350 words. [PASTE POLICY] |
| Check two documents against each other | Compare these two documents and list every place where they contradict each other on the same subject. Quote both versions side by side and say which one is more specific. Do not resolve the contradictions for me. [PASTE BOTH] |
The gap analysis prompt is the one I would run before writing anything. It is cheap, it takes one paste, and it surfaces the policies most small employers discover they need only when something has already gone wrong.
Performance Review Prompts
The problem a model solves in review season is phrasing, not judgment. You already know what you think. What is hard is saying it in language that is specific, observable, and does not read as a personality assessment.
Strip names before pasting review notes and use initials or a role label. The content of a review is exactly the kind of material that should stay inside systems you control, which is a point I come back to below.
Difficult Conversation Prompts
Use a model to rehearse, not to script. The value is in pressure testing what you plan to say and anticipating the responses, because the part that goes wrong in a hard conversation is almost never the opening.
Two boundaries. Never paste anything from a workplace investigation, including what somebody told you in confidence, and never ask a model to decide whether a complaint has merit. Rehearsal is a safe use; adjudication is not.
Survey Question Prompts
Survey writing is the task most improved by a second pair of eyes, and a model is a competent second pair of eyes for question design. It reliably catches leading questions, double-barreled questions, and questions nobody can act on.
Anonymity survives or fails at the paste. If a comment names a manager or describes a situation only one person could be in, remove it before the text goes anywhere.
What Never Goes Into a General Purpose AI Tool
Five categories of information should never be pasted into a general purpose chat window, regardless of how convenient it would be. None of them improves the quality of a draft, and all of them move sensitive data outside systems you control.
The reasoning is not only about training data. Consumer accounts may retain conversations and use them to improve the service unless you change the setting, and business tiers typically exclude inputs from training by default. Either way, the information has left your records and you cannot search it, produce it on request, or delete it with confidence.
Employee medical information carries a specific federal obligation. Employers must treat medical information obtained from any disability related inquiry or examination as a confidential record kept separate from ordinary personnel files, which is set out in the EEOC enforcement guidance on disability related inquiries. A chat log is not a confidential separate file, and it is not somewhere you can demonstrate access control.
Practically, this means most HR uses of a general purpose model should be prospective rather than retrospective. Drafting a letter you are about to write is safe. Summarizing a file you already hold usually is not, because the file contains exactly the material that should stay in your own records.
A short written rule beats a training session. Mine is four lines: no names, no numbers that identify a person, nothing medical, nothing from an investigation. Anyone can hold that in their head at the moment they are about to paste, which is the only moment it matters.
Every Draft Needs a Human Reviewer
Everything a model produces is a first draft, and the fluency of the writing is not evidence that the content is right. Legal and policy content in particular has to be read by a person before it reaches an employee.
Three failure modes account for nearly all of the damage. Invented specifics, where the draft contains a number, a deadline, or a benefit you never supplied. Wrong law, where a statute is cited that does not say what the sentence claims or that no longer applies in your state. And borrowed voice, where the document reads like a company ten times your size, which employees notice immediately.
There is a harder line worth understanding, and it is the line between drafting and deciding. Using a model to write interview questions is drafting. Using it to score, rank, filter, or shortlist candidates makes it part of a selection procedure, and the federal Uniform Guidelines on Employee Selection Procedures treat any measure used as a basis for an employment decision as subject to adverse impact analysis. Nothing about that changes because the measure is software.
A growing number of states and cities regulate automated employment decision tools directly, and the pattern is consistent even where the details are not: notice to candidates, some form of bias testing, and a record you can produce. New York City requires employers using an automated employment decision tool to have it independently bias audited within the previous year, publish a summary of the results, and notify candidates in advance, under the city rules on automated employment decision tools. Illinois has amended its human rights statute to address AI in employment decisions and to require notice. Colorado passed a broad artificial intelligence act treating employment as a high risk use, then amended it and moved the effective date more than once, which is itself a useful signal about how settled this area is.
If you want a governance frame rather than a jurisdiction checklist, the NIST AI Risk Management Framework is voluntary, free, and organized around four functions: govern, map, measure, and manage. For a small business the useful takeaway is the first one. Decide in writing what these tools may and may not be used for before anyone starts using them, rather than after.
Used this way, a general purpose model is a genuine advantage for a business without an HR department. It removes the blank page, which is the actual barrier, and it produces structure faster than any of us write it. What it does not do is take responsibility. That part is still yours.
Frequently Asked Questions
What is the best ChatGPT prompt for HR tasks?
There is no single best prompt, but every prompt that works has the same four parts: a role, the context only you have, the constraints, and the output format. The role sets the register. The context is your headcount, industry, state, and what is already true in your business today. The constraints are the negative instructions that stop a model filling gaps confidently, such as do not invent requirements I did not give you and do not cite statutes. The format is the shape of the answer, such as six bullets, under 120 words, or a two column table with placeholders in square brackets. A prompt missing the context section produces a generic page. A prompt missing the constraints produces confident detail you never supplied.
Can I use ChatGPT to write job descriptions?
Yes, and it is one of the highest value uses for a small employer, provided you feed it your real duty list rather than asking it to imagine the role. The useful sequence is three prompts rather than one: draft from your rough notes, then ask the model to flag every requirement that is not genuinely needed in the first ninety days, then ask it to identify wording that could discourage qualified applicants. The second and third passes are where the value is, because inflated requirements shrink an applicant pool without improving hire quality. Review the pay range, the location, and any physical requirements yourself before the posting goes live.
Is it safe to put employee information into ChatGPT?
No, not into a general purpose consumer tool. Five categories should never be pasted in: employee and applicant identifiers such as Social Security numbers, dates of birth, home addresses and bank details; medical and disability information of any kind; anything from a workplace investigation, including names and witness statements; salary data tied to named people; and third party confidential information covered by an agreement you signed. Consumer accounts may retain conversations and use them to improve the service unless you change the settings, and even with retention off, the data has still left your systems. Strip names to initials or role labels, remove identifying details, and describe situations generically.
Can AI write an employee handbook or a company policy?
AI can produce a readable first draft of a policy, but it cannot decide what your policy should be, and it should not be trusted on the law. The reliable approach is to describe the practice you actually follow today and ask the model to turn it into policy language, with an explicit instruction to mark anything that varies by state as a placeholder rather than guessing. Tell it not to cite statutes at all, because invented or outdated citations are one of the most common failure modes. Anything with legal effect, including leave, pay, classification, discipline, and termination language, needs review by a person who knows your state law before it reaches employees.
Can I use AI to screen or rank job candidates?
Drafting and screening are different activities with different legal exposure, and the line matters. Using a model to write interview questions is drafting. Using it to score, rank, filter, or shortlist applicants makes it part of a selection procedure, and the federal Uniform Guidelines on Employee Selection Procedures treat any measure used as a basis for an employment decision as a selection procedure subject to adverse impact analysis. Several jurisdictions add their own rules on top. New York City requires an independent bias audit and advance notice to candidates for automated employment decision tools. Illinois has amended its human rights statute to address AI in employment decisions and require notice. Check your own state and city before automating any decision.
How do I stop ChatGPT inventing things in HR documents?
Give it negative instructions in the prompt and then verify the output anyway. The three instructions that do the most work are: use only the information I have given you, do not add requirements or terms I did not supply, and do not cite statutes, regulations, or case law. Asking a model to tell you when it is unsure also helps, for example telling it to list any sentence where simplifying may have changed the meaning. After that, read the draft specifically hunting for specifics you never supplied: numbers, deadlines, dollar figures, legal references, and named benefits. Every number and date in an HR document should be checkable against your own records before it goes out.
What HR tasks should I not use AI for at all?
Decisions, investigations, and anything where the answer depends on facts the model cannot see. Do not use it to decide who to hire, who to promote, who to discipline, or who to let go. Do not use it to analyze a complaint or summarize investigation material. Do not use it to determine whether an accommodation is reasonable, whether an employee is exempt from overtime, or how a state leave law applies to a specific person. Those are judgment calls with legal consequences, and a fluent wrong answer is more dangerous than no answer. Drafting, rewriting, structuring, and pressure testing your own thinking are the safe uses.
Do I need a paid or business AI account for HR work?
A business tier with administrative controls is a meaningful improvement over a personal consumer account, mainly because of data handling and settings you can enforce across a team rather than trusting each person to configure. Business and enterprise tiers typically exclude your inputs from model training by default and give an administrator visibility over accounts. That said, a paid account does not change what belongs in the tool. The five red line categories stay off limits regardless of tier, because the risk is not only training data, it is that identifying information about your employees now exists in a system you do not control and cannot search or purge on request.