Prompt Engineer hiring kit

Every step to hire a prompt engineer who can prove a change to an instruction actually worked — not just recite the vocabulary.

About this kit — the 5Ws

A prompt engineer owns the instruction layer — the prompts, system messages and agent instructions that tell a model what to do — and keeps a case set: a repeatable list of test inputs with the outputs you'd expect, used to check whether a change actually helped. The job is less about elegant phrasing and more about a measurement habit: form a hypothesis, test it against the case set, and report honestly what improved and what got worse. Hire for that discipline, not for fluency in the vocabulary.

Calibrated for:: Mid-level through senior, individual contributor track for this instruction-layer specialism.
Timeline:: Set an approval-to-offer target at intake, allowing for panel availability, candidate adjustments, references and any lawful checks.

Who: Hiring managers and recruiters bringing in a dedicated prompt engineer, where prompt and instruction quality sits close to a live product rather than as a side task for whoever is free.
What: Use this kit to test instruction measurement, regression handling, model-behaviour diagnosis and safe iteration.
When: Use it once you've confirmed the role is worth staffing on its own — before the requisition opens — and stay with it until you've worked out where the hiring process lost time.
Where: Technology teams with a live instruction layer that needs a dedicated owner.
Why: The work sample, structured questions and rubric let you compare job-related evidence consistently. For this role, that means checking whether a candidate actually measured an instruction change before claiming it worked, rather than judging by how confidently they talk about it.

The competency model · 6

Use these competencies to compare job-related evidence. Review any apparent gap in a must-have before deciding.

Evaluation and measurement disciplineMust-have · 25
Builds a case set — a repeatable list of test inputs and the outputs you'd expect — before trusting a prompt change, and can show results before and after, including anything that got worse.
Systematic iterationMust-have · 20
Forms a hypothesis about why an output failed, controls changes where practical, runs the case set enough times to see whether a result holds, and can explain why they stopped rather than kept tweaking.
Model-behaviour diagnosisMust-have · 20
Can tell whether a wrong output comes from the prompt itself, from the system handing the model the wrong background material (a retrieval problem), from missing information in that material (a context problem), or from the underlying data — and says so before touching anything.
Instruction robustness and versioningCan learn · 15
Writes instructions that keep working after a model update or an unusual input, and tracks versions so a regression can be traced to a specific change.
Cross-team communicationMust-have · 10
Explains what an instruction can and cannot fix to non-technical stakeholders, and pushes back on requests that measurement doesn't support.
Safety and misuse awarenessCan learn · 10
Notices instruction-following risks — embedded instructions, data leakage, harmful output — and designs against them without being asked.

What's required and what's optional

Your hiring journey for Prompt Engineer. Select a stage to jump to it.

Required for every hire
Optional — use when it adds evidence
AI boost — a big drafting win
Colour = phase

Phase 1
Plan
1. 01Plan the roleRequired
2. 02Agree successRequiredAI boost
Phase 2
Attract
1. 03Write the jobRequiredAI boost
2. 04Find candidatesOptionalAI boost
Phase 3
Select
Phase 4
Decide
Phase 5
Onboard & learn
1. 12Help them settle inRequired
2. 13Measure time to hireRequiredAI boost

AI drafts, people decide: Use AI to draft, rewrite and summarise your own material — never to rank, score, screen out or decide on candidates, and never with a candidate's personal data.

Plan

Attract

Select

Decide

Onboard & learn

Solid = required · dashed = optional · arrow keys move between steps

Stage 1 of 13Required

0 of 3 done

Plan the role

Workforce planning and requisition approval

Who runs it: Hiring manager + finance / HR business partner
Done when: An approved requisition with a written business case, budget and a named decision-maker.

Key tool

Answer these before the role is approved

1Which instruction layers or prompts will this person own in their first year?
2How do we know today whether those instructions are working?
3Could an existing engineer pick up the instruction layer and its measurement in the next two quarters?

AI as an advantage4D framework

Delegate: Hand AI the first draft of the business case and the build / borrow / buy comparison. The decision, the budget and the sign-off stay with people.

Show the prompt and the checks

Describe: You are helping a hiring manager write a business case for a Prompt Engineer role. Context: I will paste the problem the hire solves, the work it involves, the capacity we already have and the time we have. Produce: (1) a one-sentence problem statement in business terms; (2) a build / borrow / buy comparison with the strongest argument for each; (3) the questions a finance partner would ask; and (4) a short recommendation with the evidence it rests on. Use British English and plain language. Do not invent figures — leave [brackets] where I need to supply numbers. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check every number and claim against your own notes and strike anything the draft assumed. Make sure 'buy' is argued from ongoing work, not from a job title.
Diligence: The hiring manager owns the final case and the budget request. No candidate information belongs here; AI drafts the case, people decide whether to open the role.

Don't miss: Opening a dedicated prompt engineer role when the actual gap is retrieval or data quality. Trace the failure to its real layer before writing the requisition.

Framework

Build · Borrow · Buy

Before opening a role, decide whether to build the capability in your current team, borrow it (contractor, agency, partner), or buy it (a permanent hire).

Write the problem the hire solves in one sentence, in business terms — not the job title.
Test Build: could an existing person grow into this within the time you have?
Test Borrow: is the need time-boxed, or does it end once a system is shipped?
Choose Buy only when the work is ongoing, core to the product and nobody inside can absorb it.
Record the decision, the budget and the one person who signs off the final hire.

Why it works: Writing the build, borrow or buy decision down exposes a vague or temporary need before it becomes a permanent requisition — and gives every later stage an anchor to check against.

• Name the final decision-maker — for example, the engineering or product lead the hire will report to.

Is this a dedicated role?

Decide whether the work is sustained and substantial enough for a dedicated owner by writing down the workload, the risk, the capacity you already have and the outcomes you expect.

Answer these before the role is approved

1.Which instruction layers or prompts will this person own in their first year?
Listen for: Named prompts or agent instructions with real users today, or committed on the roadmap.
Red flag: 'We want our prompts to be better' with nothing specific.
2.How do we know today whether those instructions are working?
Listen for: An honest answer. If it's 'we don't really know', that gap is the job you're hiring for.
3.Could an existing engineer pick up the instruction layer and its measurement in the next two quarters?
Listen for: A named engineer with time set aside to learn it, or a clear reason the gap needs a dedicated hire instead.
4.Is the need ongoing, or does it end when one instruction set ships?
Listen for: Ongoing work (evaluation, iteration, maintaining behaviour across model updates) justifies a permanent hire; a single rewrite may suit a contractor.
5.What does it cost us, month over month, while nobody owns this instruction layer?
Listen for: A concrete consequence: inconsistent answers reaching customers, support escalations, nobody able to say whether a change helped.

Build, borrow or buy?

Build: have an existing engineer spend a ring-fenced day or two a week building a case set and measuring the instructions already in production, with a mentor if one is available.
Borrow: bring in a specialist for a fixed engagement to stand up the case-set discipline and instruction-versioning approach, then hand it to your own team.
Buy: hire a permanent prompt engineer once several instruction layers need continuous care and nobody currently owns proving whether a change helped.

Watch out for

Confusing this role with an AI engineer. If the hardest problem is the system around the model — retrieval, tool calls, deployment — you need an AI engineer; if it's the instruction layer itself and the evaluation behind it, you need a prompt engineer.

Stage 2 of 13Required

0 of 2 done

Agree success

Intake meeting and competency model

Who runs it: Recruiter + hiring manager
Done when: A signed-off competency model: 5–7 competencies, each marked must-have or trainable, with weights.

Key tool

Ask the hiring manager

1Which of the systems or areas this person will own worries you most, and why?
2When something there went wrong recently, how did you find out?
3In 12 months, what will be shipped, fixed or running for you to call this hire a success? Agree two or three measurable outcomes.
4Who will they work with most closely?
5What could a strong candidate learn in their first three months? Those capabilities are 'trainable' — never use them to filter applications.

AI as an advantageAI boost4D framework

Delegate: Hand AI your raw intake notes to turn into a draft competency model. Choosing the must-haves and the weights stays with the hiring manager and recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter and a hiring manager build a competency model for a Prompt Engineer role. Context: I will paste the notes from our intake meeting, including the 12-month outcomes and what the hiring manager worries about most. Produce a table of 5–7 observable competencies with, for each: a one-line definition; whether it is a must-have or trainable, with the reason; what evidence of it looks like in real work; and a suggested weight so the weights total 100. Use British English and inclusive wording, avoid years-of-experience or named employers as evidence, and keep every competency job-related. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each competency is observable and job-related, that nothing describes a type of person rather than the work, and that trainable items haven't been marked must-have.
Diligence: The hiring manager signs off the final model and weights before anyone meets a candidate. No candidate data goes into the prompt; AI drafts, people decide.

Don't miss: Listing a specific prompting technique or framework as a must-have. Techniques change with every model release; the underlying discipline doesn't.

Framework

Competency model (must-have vs trainable)

A short list of the observable capabilities the job needs, split into what the hire must bring on day one and what you will teach.

Start from the 12-month outcomes, then ask which capabilities produce them.
Keep 5–7 competencies. With more, interviewers struggle to score each one separately.
Mark each must-have or trainable. Only must-haves can count against a candidate.
Give each a weight so they sum to 100, agreed before anyone meets a candidate.
Write what evidence of each looks like, so interviewers know what they are listening for.

Why it works: Agreeing the criteria before meeting candidates stops the bar moving from one candidate to the next, and makes it harder for 'I'll know it when I see it' to decide the hire.

Core intake questions (ask for every role)

Book 45 minutes with the hiring manager before anything is advertised, and write the answers down.
Which of the systems or areas this person will own worries you most, and why?
When something there went wrong recently, how did you find out?
In 12 months, what will be shipped, fixed or running for you to call this hire a success? Agree two or three measurable outcomes.
Who will they work with most closely?
What could a strong candidate learn in their first three months? Those capabilities are 'trainable' — never use them to filter applications.
Who is on the interview panel, and is their time held for the next four to six weeks?

Questions specific to this role

1.Which model provider(s) or orchestration tools does this instruction layer actually run on today?
Listen for: Concrete tooling context that shapes the work sample and the systems round of the interview.
2.Has anyone tried measuring these instructions before, even informally? What did they use, if anything?
Listen for: Whether there's an existing measurement habit or tooling to build on, or the hire is starting from zero.
3.Is this instruction layer customer-facing, internal, or both?
Listen for: How much a wrong output actually costs — it sets the weight on safety and misuse awareness.
4.Who currently reviews or approves a change to these instructions before it ships, if anyone?
Listen for: Whether a review process already exists, or the hire will need to build one from scratch.

What success looks like in 12 months

Every prompt and agent instruction running in production has a case set that runs automatically before any change ships.
At least one specific instruction, named at intake, has a measured before-and-after improvement, with any regression counted alongside the win.
Instruction versions are logged well enough that a support ticket about a bad answer can be traced back to the exact change that caused it.
Support and product can point to the current instruction set and say, without asking engineering, what it's meant to do and where it's known to fall short.

Watch out for

Letting model training or fine-tuning requirements sneak into the role. This job is about the instruction layer and proving whether a change to it worked, not building or tuning the underlying model.

Stage 3 of 13Required

0 of 3 done

Write the job

Job description and pay band

Who runs it: Hiring manager (content) + recruiter (publishing)
Done when: A published job description that states outcomes, the must-haves, how AI tools are used and the pay information the law requires.

Key tool

Prompt Engineer

We're looking for a prompt engineer to own the instructions behind [feature/product] and the case sets used to check whether changes improve results. You'll work with product, support and engineering as the underlying models change.

What you'll do

•Build and maintain case sets that measure whether a prompt or instruction set is working, and run them before shipping any change.
•Diagnose whether a wrong output is an instruction problem, a retrieval or context problem, or a data problem before changing anything.
•Iterate on prompts and agent instructions with a clear hypothesis for each change, testing it against the case set and reporting what improved, what got worse, and how confident the result is.
•Version instructions and track which change caused which shift in behaviour, so regressions can be traced quickly.
•Explain what an instruction can and cannot fix to product, support and other non-technical stakeholders.
•Watch for instruction-following risks — embedded instructions in user content, data leakage, harmful output — and design against them.

What you need

•You can show a case set you built and how you used it to judge whether a change to an instruction helped.
•You can describe a time you diagnosed whether a bad output was a prompt problem or something else in the system, and how you told the difference.
•You iterate with a hypothesis and a measurement, not by feel — and you can say when a result needs checking again before you trust it.
•You can explain a technical trade-off to someone without a technical background.

Nice to have

•Experience maintaining instructions across multiple model versions or providers.
•Experience with agent instructions that call tools or other systems.
•Experience with retrieval or context design alongside prompt work.
•Familiarity with how AI regulation, such as the EU AI Act, touches the instruction layer specifically.

How we use AI tools

•This work is unusually meta: you'll often use an AI tool to help draft or test the very instructions you're evaluating. Treat its suggestions as a draft to check against your case set, not a finished answer.
•You'll know when a problem isn't actually a prompting problem, and say so rather than trying to prompt around it.
•In the work sample, use whatever tools you'd use on the job — just say which ones and how you checked what they gave you.

Pay

Salary range: [currency, minimum–maximum], plus [equity/bonus/benefits]. The range reflects [location/working pattern]. We place every offer within it using the same pre-set, job-related, gender-neutral factors — Scope of the instruction layer they'll own, Depth of evaluation and measurement experience, Model-behaviour diagnosis experience across a live product, Experience maintaining instructions across model or provider changes — never previous salary or negotiating style.

AI as an advantageAI boost4D framework

Delegate: Hand AI the first draft of the job description from your signed-off competency model. The must-haves, the pay range and the final wording stay with people.

Show the prompt and the checks

Describe: You are helping write an outcome-based job description for a Prompt Engineer role. Context: I will paste the competency model (must-have and trainable, with evidence), the 12-month outcomes, how the team uses AI tools, the location and working pattern, and the pay range. Produce: a summary paragraph that leads with the problem and what the hire will own; 'What you'll do' as outcomes, not tasks; 'What you need' containing only the must-haves; 'Nice to have' for the trainable items; 'How we use AI tools'; and a pay section that states the range. Use British English and inclusive, gender-neutral wording, avoid hype, and replace any 'X years' with the capability it stands for. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that 'What you need' contains only the must-haves, that no degree, employer or years requirement crept in, and that the pay range and location are exactly what was approved.
Diligence: The hiring manager owns the content and the recruiter owns publishing, including the pay information the law requires for each location. AI drafts the advert; people decide what it says.

Don't miss: Asking for a number of years of 'prompt engineering experience'. The discipline is only a few years old at most; the requirement just filters for people willing to round up.

Stage 4 of 13Optional

0 of 2 done

Find candidates

Sourcing strategy

Who runs it: Recruiter / sourcer
Done when: A shortlist-ready pipeline from at least three channels, with outreach tracked.

Use when:: Inbound applications won't give you enough qualified candidates.
Skip when:: Your shortlist already comes from inbound applications or referrals.

Key tool

Search query

("prompt engineering" OR "prompt design" OR "instruction tuning" OR "system prompt") AND ("evaluation" OR "eval set" OR "test cases") AND ("production" OR "shipped" OR "deployed") NOT ("intern" OR "student")

First outreach message

AI as an advantageAI boost4D framework

Delegate: Hand AI the search queries and the first draft of the outreach template. Choosing channels, personalising to a candidate's public work and sending stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter source candidates for a Prompt Engineer role. Context: I will paste the must-have competencies, the kind of work we want evidence of, the channels we are using and the pay range. Produce: (1) one search query per channel, using OR for alternatives, AND for things that must all appear and NOT to exclude, with a one-line note on what each query looks for; and (2) a first-outreach template of under 120 words that opens with a [placeholder for something the candidate built], states the problem we are hiring to solve and the pay range, and ends with a low-pressure question. Use British English and inclusive wording. Do not refer to any specific person. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the queries search for evidence of the work rather than job titles alone, and that the template claims nothing about the role or the pay you can't back up.
Diligence: The recruiter personalises every message to something the candidate actually built, and sends it. No names or profile data go into the prompt; AI drafts, people decide whom to contact.

Don't miss: Filtering candidates out because their profile doesn't use the words 'prompt' or 'instruction'. Search for the case sets, evaluation write-ups and shipped changes instead of the vocabulary.

Framework

Channel mix + evidence-first outreach

Spread sourcing across channels where the work is visible, and open outreach with the problem, not the perks.

Pick channels where candidates show their work, not only where they list job titles.
Write one search query per channel. OR joins alternatives, AND joins things that must all appear, NOT excludes. Keep the version that works best.
Personalise the first line to something the candidate actually built or wrote.
Lead with the problem and the pay range. Save the perks for later.
Track reply rates by channel each week and drop the channel that isn't working.

Why it works: Searching for evidence of the work can surface candidates whose current title differs from the vacancy, or whose CV undersells what they have done.

Sourcing basics

Adapt the search query for each platform — the same words behave differently on each site.
Candidates with adjacent titles may have relevant evidence. Search for the work and outcomes the role needs, not the current title.
Channel activity is a lead, not evidence. Review linked work and examples against the written competencies.

Where to look

Public repositories and prompt or evaluation tooling contributions

Contributions to evaluation harnesses or instruction-management tools are reviewable evidence of the skills this role needs.

Technical write-ups on model-behaviour debugging

A write-up on diagnosing a failure, and how the fix was measured, is reviewable evidence of that discipline.

Your own engineering team's referrals

A colleague who reviewed the candidate's instruction changes can point you to specific versions and test results to look at.

LinkedIn and similar networks, searched for case sets and evaluation write-ups rather than a title

Titles vary for this work. Search for candidates describing a case set, an evaluation harness or a shipped instruction change, not the words 'prompt engineer'.

Communities around a specific model provider or orchestration tool

Look for members answering technical questions about instruction design and evaluation, with links to what they actually built.

Boolean search string

("prompt engineering" OR "prompt design" OR "instruction tuning" OR "system prompt") AND ("evaluation" OR "eval set" OR "test cases") AND ("production" OR "shipped" OR "deployed") NOT ("intern" OR "student")

First outreach message

Hi [name] — [one detail from their post/repo] caught my eye, particularly how you [specific decision or result]. At [company] we've got [n] instruction layers live in production and honestly can't always tell when a change to them helps or hurts. That's the gap we're hiring a prompt engineer to close, paying [range]. Open to a 20-minute call to hear what the problem looks like from the inside?

Stage 5 of 13Required

0 of 3 done

Check applications

Application screening

Who runs it: Recruiter
Done when: Every application reviewed against the same written knockouts within five working days.

Key tool

Reject only if one of these is missing

•Can meet the role's work-authorisation requirements by the start date, with sponsorship where the employer offers it. Don't ask for documents at this stage.
•Describes at least one instruction or prompt change that shipped to real users.
•Can point to some form of measurement behind that change — a case set, a metric, a structured comparison.

AI as an advantage4D framework

Delegate: Hand AI the job of turning your written knockouts into a consistent checklist with reason codes. Reviewing each application stays with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter prepare an application-screening checklist for a Prompt Engineer role. Context: I will paste the must-have competencies and our written knockout criteria. Produce: (1) each knockout as a yes/no question answerable from an application alone; (2) a short list of reason codes for a 'no'; and (3) a note of any criterion that looks like a proxy — a degree, employer brand or employment gap — rather than a job-related requirement, with a suggested rewrite. Use British English. Do not evaluate any applicant and do not ask for applicant details. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each question is binary and job-related, and that every flagged proxy has been removed or justified in writing.
Diligence: A person reviews every application against the checklist and records the reason. Never paste CVs or applicant details into an AI tool, and never let a tool screen anyone out — in New York City and the EU that can bring automated-decision rules into play.

Don't miss: Rejecting a candidate because their CV lists prompting vocabulary without a shipped, measurable change — ask for the specific regression they caught, not the terminology.

Framework

Knockout criteria

A small set of yes/no requirements, written in advance, applied to every applicant the same way.

Derive knockouts only from must-have competencies and genuine legal requirements.
Keep them binary and answerable from the application itself.
Apply them in the same order to every application and log the reason for each rejection.
Do not use proxies such as university, employer brand or employment gaps as knockouts.
Anything ambiguous gets a second review or moves forward to the skills stage.

Why it works: Early screening rejects people before richer evidence exists, so its criteria need the most care. Written criteria make decisions reviewable — but each one must also be job-related, applied consistently and checked for unjustified adverse impact.

Screening rules

Do not use degrees, employment gaps or certifications as filters unless they are demonstrably job-related and justified for this role.
Judge the claims in a CV, not how polished it is — a well-written CV may or may not have had AI help, and either way it is the evidence that matters.

Knockout criteria

Can meet the role's work-authorisation requirements by the start date, with sponsorship where the employer offers it. Don't ask for documents at this stage.
Describes at least one instruction or prompt change that shipped to real users.
Can point to some form of measurement behind that change — a case set, a metric, a structured comparison.

Green flags

Describes how they told a prompt problem apart from a retrieval, data or system problem.
Mentions a change that made things worse and what they did about it.
Links to a case set, write-up or demo you can actually check.

Red flags

Lists prompting techniques and vocabulary with no outcome attached to any of them.
Everything they describe is a personal project or class exercise, with no real users ever seeing the output (acceptable for a junior hire, not this level).
Claims that can't be tied to a specific instruction or system.

Watch out for

Treating fluent talk about prompting vocabulary as evidence on its own. Ask what they actually measured.

Compliance checkpointNew York CityEU

For a New York City employment decision, work out whether a tool is an 'automated employment decision tool' (AEDT) under Local Law 144 — one that uses covered techniques to substantially assist or replace a discretionary decision. Before using a covered tool, make sure an independent bias audit was completed no more than a year earlier, publish the required summary, and give covered candidates or employees the required notice at least ten business days before use. In the EU, the AI Act (Annex III) lists AI used to recruit, filter applications or evaluate candidates as high-risk, subject to the Article 6(3) exception; the related obligations apply from 2 December 2027 under the amended timetable. A manual screen does not exempt an AI tool used elsewhere in the process.

Sources: NYC DCWP — Automated employment decision tools · EU AI Act — consolidated Regulation (EU) 2024/1689 · checked 2026-09-25

Stage 6 of 13Optional

0 of 2 done

Recruiter call

Recruiter screen

Who runs it: Recruiter
Done when: A 20–30 minute call that confirms logistics, motivation and one or two must-have signals.

Use when:: You are hiring at volume, or pay and logistics need confirming early.
Skip when:: You have only a handful of applicants — fold the pay and logistics questions into application screening instead.

Key tool

Ask every candidate, in this order

1Walk me through the last instruction or prompt change you shipped to production — what triggered it?
2How did you know it actually helped?
3Tell me about a change you made that turned out to make things worse. What happened next?
4When an output is wrong, how do you start working out whether it's the instructions or something else feeding the model?
5Our range for this role is [range]. Does that work for you?

AI as an advantage4D framework

Delegate: Hand AI the first draft of the screening-call script and its evidence anchors. Running the call and taking the notes stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter write a 20–30 minute structured screening call for a Prompt Engineer role. Context: I will paste the must-have competencies, the logistics we need to confirm (location, working pattern, notice period) and the pay range. Produce: an opening that explains the process, the timeline and the pay range; 4–6 questions in a fixed order, each with one line on what a strong answer contains; and a closing that asks whether the published range works. Use British English and inclusive wording. Do not include questions about salary history, age, family, health or any other protected characteristic. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that every question is job-related and lawful, that the pay question asks whether the range works rather than for history, and that the anchors describe evidence, not impressions.
Diligence: The recruiter asks the same questions of everyone and writes the notes. No candidate details go into the prompt; AI drafts the script, people run the call and decide.

Don't miss: Expecting the recruiter to judge whether a candidate's measurement approach was rigorous. That call belongs to the work-sample marker and the panel, not the screening call.

Framework

Structured screening call

The same short set of questions for every candidate, with notes taken against what a strong answer contains.

Open by explaining the process, timeline and pay range. No surprises later.
Confirm logistics: location, working pattern, notice period and whether the range works for them.
Ask the same 4–6 questions in the same order, and note evidence, not impressions.
Leave time for the candidate's questions. Don't score their questions unless they test a written, job-related competency.
Decide within 48 hours and tell the candidate either way.

Why it works: Using the same questions and written evidence anchors makes answers easier to compare, and a short, predictable call respects the candidate's time.

Screening call basics

Set the decision timetable before sourcing and tell candidates when to expect each update.
Ask every candidate what draws them to this particular role, and listen for interest in the work described rather than the title.
Confirm notice period and whether they are in other processes, so you can plan the timetable.
End every call by asking whether the published pay range works — never ask for salary history.

Recruiter screen questions

1.Walk me through the last instruction or prompt change you shipped to production — what triggered it?
Listen for: Names the specific instruction, who used it, and their own decisions in shipping it — not just 'the team' did it.
Red flag: Describes the change only as something 'we' shipped, with no personal decisions, trade-offs or measurement they can point to.
2.How did you know it actually helped?
Listen for: Some form of structured comparison before and after, however simple — not just a subjective read.
Red flag: 'It read better' or 'people stopped complaining'.
3.Tell me about a change you made that turned out to make things worse. What happened next?
Listen for: A specific example and an honest account of what they did once they saw it wasn't working.
Red flag: Claims every change they've shipped has worked, or can't recall a regression.
4.When an output is wrong, how do you start working out whether it's the instructions or something else feeding the model?
Listen for: Some basic instinct to check what the model was actually given before rewriting the prompt.
Red flag: Goes straight to 'I'd reword the prompt' with no mention of checking anything first.
5.Our range for this role is [range]. Does that work for you?
Listen for: A clear answer on whether the published range works. Don't ask for salary history — this process sets pay from the published band and pre-set, job-related factors.

Compliance checkpointUS (state and city)EU

Don't ask for salary history. In the EU, national laws implementing Article 5(2) of Directive (EU) 2023/970 must prohibit the question. US rules vary, but jurisdictions including California and New York prohibit employers from seeking salary history. Ask whether the published range works instead.

Sources: EU Pay Transparency Directive 2023/970, Article 5 · California Labor Commissioner — salary history · New York State DOL — salary history ban · checked 2026-09-25

Stage 7 of 13Required

0 of 1 done

Work sample

Skills assessment / work sample

Who runs it: Hiring manager (design) + recruiter (logistics)
Done when: Every shortlisted candidate completes the same job-shaped task, marked against criteria written in advance.

Key tool

Diagnose and improve a drifting meeting-notes assistant

You'll get a small internal tool that turns raw meeting transcripts into follow-up emails with action items, along with 15 real-looking transcripts to test it on. Treat the 15 as an exploratory set, not a statistically reliable sample — report counts of what you find, not percentages or rates. Some outputs invent an action item nobody raised. Others drop the owner or the date. One transcript has a line planted inside it, to see whether the assistant follows an instruction buried in the meeting content rather than the task you were actually given. Build a way to measure how often each problem shows up across your 15 cases, make one or two targeted changes to the instructions, and report honestly whether your changes helped.

Planned for up to three hours (adjustments available on request). At the agreed stopping time, write a short note on what you would examine next, and what you deliberately left out.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the work-sample brief and its marking sheet. Choosing the task, setting the timebox and marking stay with the hiring manager.

Show the prompt and the checks

Describe: You are helping a hiring manager design a work sample for a Prompt Engineer role. Context: I will paste the competency model, a real problem the team solved recently with anything confidential removed, the timebox, and our AI-tool policy for candidates. Produce: a candidate-facing brief (what to do, what to hand in, the time cap, how to request an adjustment, which AI tools are allowed and how to report their use); and a marking sheet with 4–6 criteria tied to named competencies, each with what a 1, a 3 and a 5 look like. Use British English and plain, inclusive wording. Do not design anything that needs more than the stated time. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the task fits the timebox, that every criterion maps to a competency, and that the brief gives all candidates the same starting materials.
Diligence: The hiring manager owns the task and the criteria, and people mark every submission. Do not paste candidate submissions into an AI tool for scoring; AI drafts the brief, people mark and decide.

H-EvaluateH-Evaluate can generate a job-specific assessment from your job description, so you don't have to build the task by hand.How H-Evaluate helps →

Don't miss: Setting an open-ended 'write us a great prompt' task. It measures writing style, not the job.

Framework

Work-sample test

A small, realistic slice of the job, marked against criteria written in advance. Give candidates equivalent materials and arrange reasonable adjustments where required.

Take a real problem the team solved recently and shrink it to fit the timebox.
Write the marking criteria first, tied to the competency model.
Timebox honestly — for take-home work, a few hours at most — and say so in the brief.
State the AI-tool policy in writing. Where the job permits these tools, use equivalent conditions in the assessment and evaluate how candidates verify their output.
Mark without CV details where you can, and have two people mark borderline submissions.

Why it works: A well-designed work sample gives direct evidence of job-relevant skill rather than a proxy for it, and gives candidates a realistic preview of the work.

Assessment rules for every candidate

State the planned time cap and how to request an adjustment. At the agreed stopping time, ask candidates to note what they would examine next and what they left out — never reward unpaid overtime.
Give every candidate the same starting materials, and use a second marker for borderline submissions.
Say in writing which AI tools are allowed and ask candidates to tell you how they used them.
Invite every candidate to ask for adjustments, and answer each request promptly.
Pay for anything the task costs the candidate to run, such as model or cloud usage.

The work sample

Diagnose and improve a drifting meeting-notes assistant

Timebox: Planned for up to three hours (adjustments available on request). At the agreed stopping time, write a short note on what you would examine next, and what you deliberately left out.

What the candidate hands in

Your measurement approach: the cases you checked and how you decided whether an output was right.
Your changes to the instructions, with a short explanation of each.
Your results as counts across the 15 cases, before your changes and after — this sample is too small to turn into percentages.

Marking criteria (write these first)

The measurement exists and checks something real, not just 'did it produce an email'.
The changes target the failures the measurement found, not general rewording.
Reports the counts honestly across the 15 cases, including any that got worse after the change.
The embedded-instruction line is noticed and handled.
The instructions are clear enough that someone else on the team could maintain them.
The half-page note shows they can prioritise under a time cap and say plainly what they left for later.

Adjustments: Candidates can ask for more time or a live, paired version instead. We'll cover the model usage — the starter files include a spending-limited key.

Watch out for

Banning AI tools during the exercise. The role expects you to use them — watch whether the candidate checks what comes back rather than shipping it unread.

Compliance checkpointUSGreat BritainEU

In the US, covered employers must provide a reasonable accommodation to a qualified applicant with a disability unless it would cause undue hardship (ADA). In Great Britain, the Equality Act 2010 requires reasonable adjustments in recruitment. In the EU, the duty comes from national equality law. Don't ask for more medical information than the law allows.

Sources: EEOC — prohibited practices · Equality Act 2010 · checked 2026-09-25

Stage 8 of 13Required

0 of 3 done

Structured interviews

Structured interview loop

Who runs it: Interview panel (named in advance)
Done when: Every competency has a lead interviewer and at least one second source of evidence, with the same questions for every candidate.

Key tool

Interview questions by pillar

Free in this kit: Domain questions for every round. The other three pillars and the AI-fluency check are measured by H-Evaluate.

Free in this kit

Senior prompt or AI engineer who marked the work sample · Evaluation and measurement discipline · Systematic iteration

1.Walk me through the cases you checked in the work sample. Why those, and not others?
Evaluation and measurement discipline
Good answerChooses cases tied to the failure types actually seen in the transcripts, states their pass/fail rule plainly, and says what the 15 cases can't tell you.
Weak answerCases chosen at random or because they were easy to check; can't say how an output is judged correct.
2.One of your changes in the work sample improved several cases and regressed another that used to pass. How would you investigate the trade-off before deciding what to keep?
Evaluation and measurement discipline
Good answerNames it as a real trade-off, checks how often the regressed case would occur outside the test set, and decides on evidence rather than on the raw counts alone.
Weak answerDoesn't mention the regression at all, or keeps rewording the instruction hoping the number moves without checking why it regressed.
3.How did you decide what to change first in the instructions, and how did you know when to stop?
Systematic iteration
Good answerNames a hypothesis about the cause, changes one thing, measures, and gives a clear reason for stopping.
Weak answerMade several changes at once, or kept adjusting until it 'felt' right.

Optional follow-ups (1)

4.If you had another hour on this task, what would you check next, and why that and not something else?
Systematic iteration
Good answerPicks the highest-risk unknown given what was already measured, and explains the trade-off against the other options.
Weak answerPicks something trivial, repeats work already done, or can't say why one option matters more than another.

AI as an advantageAI boost4D framework

Delegate: Hand AI the drafting of probe questions and strong / weak anchors for one competency. Asking, listening and scoring stay with the interviewer.

Show the prompt and the checks

Describe: You are helping an interviewer prepare a structured technical interview for a Prompt Engineer role. Context: I will paste one competency, its evidence description and the core question we ask every candidate. Produce: four probes in this fixed order — what would you do, why that choice, what else did you consider, how would you know it worked — tailored to the question; a one-line description of a strong answer and of a weak answer; and two follow-ups to use only if time allows. Use British English and inclusive wording. Keep everything about the work, nothing about the person. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the probes test the named competency rather than trivia, that the anchors describe observable evidence, and that no question touches a protected characteristic.
Diligence: Interviewers ask the same core questions of every candidate and score alone against the rubric. Interview notes and candidate names never go into an AI tool; AI drafts the questions, people interview and decide.

H-EvaluateH-Evaluate can add evidence on Cognitive, Situational judgement, Behavioural and AI fluency for your panel to review alongside these interviews.How H-Evaluate helps →

Don't miss: Scoring 'model-behaviour diagnosis' on how the candidate talks about layers, without asking them to actually locate the fault in a specific example.

Framework

Structured technical interview

Every candidate gets the same job-specific questions, each interviewer leads on specific competencies, and every answer is probed with the same four follow-ups: what would you do, why, what else did you consider, and how would you know it worked.

Give each competency a lead interviewer, plus a second source of evidence — the work sample, another round or an independent marker. Avoid asking the same thing twice, but never let one person's view be the only evidence for a critical competency.
Ask the three core questions for your round, in order, for every candidate. Use an optional follow-up only if there is time.
Probe each answer: why that choice, what else they considered, and how they would know it worked — until you hear the candidate's own technical decision.
Take notes on evidence during the interview. Don't rate until it's over.
Fill in your interviewer sheet and submit it before you talk to any other interviewer.

Why it works: Structured interviews make answers comparable: every candidate faces the same job-related questions and is scored against the same anchors.

Interview rules

Use the same core questions in the same order for every candidate.
In the work-sample review, understand the candidate's decisions against the rubric — don't turn it into a debate about your preferred solution.
Avoid trivia disconnected from a real job decision. Use a realistic debugging, design or evaluation problem instead.

Compliance checkpointUSUKEU

Don't ask about protected characteristics — such as age, disability, pregnancy or family plans, religion, race or nationality — unless applicable law permits the question for a specific, necessary, job-related purpose, and HR or counsel has confirmed that basis beforehand. Ask only whether the candidate can meet the role's work-authorisation requirements. If a candidate volunteers protected information, don't probe; record only what is lawfully needed, keep it separate and restrict access.

Sources: EEOC — prohibited practices · Equality Act 2010 · checked 2026-09-25

Where to find more interview questions

Use web question banks for inspiration only: ask every candidate the same job-related questions, check each one against this kit's competencies, and never ask about protected characteristics. Don't reuse this kit's work sample from a public bank — candidates can find questions that circulate online, so a widely posted question tests preparation more than skill.

Show 7 sites

US OPM — Structured interviews(opens in a new tab)
US federal guidance on building structured interviews, with a guide, training material and sample questions with rating scales.
FreeFor interviewers
Acas — Interviewing job applicants(opens in a new tab)
UK public guidance on preparing fair interviews, asking applicants the same questions and avoiding discriminatory ones.
FreeFor interviewers
Workable — Interview questions library(opens in a new tab)
Sample interview questions for employers, listed by job role, including machine learning, data and engineering roles.
FreeFor interviewers
Indeed for Employers — Interview questions(opens in a new tab)
Sample interview questions for employers by job title and skill, with notes on what to look for in answers.
FreeFor interviewers
Tech Interview Handbook(opens in a new tab)
Open-source software-engineering interview preparation: coding, system design and behavioural questions.
FreeFor candidates and interviewers
Chip Huyen — Machine Learning Interviews Book(opens in a new tab)
Free online book with knowledge questions on maths, computer science and machine learning, grouped by difficulty.
FreeFor candidates and interviewers
Blind — Interview questions channel(opens in a new tab)
Anonymous posts by tech workers about questions they report being asked. Unverified; some content needs sign-in.
Free, some content needs sign-inCandidate reports, unverified

Links checked 2026-10-05. External sites; we don't control their content.

Stage 9 of 13Required

0 of 2 done

Compare evidence

Scoring, calibration and debrief

Who runs it: Hiring manager (chairs) + panel
Done when: A documented hire / no-hire decision traceable to scored evidence against the competency model.

Key tool

Interactive scorecard

Score one candidate against the rubric. Everything stays in your browser. Download it as a spreadsheet for your records.

Evaluation and measurement discipline Must-have

Weight

What do 1, 3 and 5 look like?

1: No case set, or only 'it reads better'. Can't say how they'd know a change helped.
3: Puts together a case set once prompted, catches the obvious failure mode, but doesn't think to check edge cases unprompted.
5: Has the case set ready before anyone asks for it, aims it at the failure types that actually occur, and volunteers the regressions without being pressed.

Systematic iteration Must-have

Weight

What do 1, 3 and 5 look like?

1: Changes several things at once, or tweaks wording until it 'feels' right.
3: Changes one thing at a time when prompted, but doesn't always form a hypothesis first.
5: Forms a hypothesis, changes one variable, measures, and can say exactly why they stopped.

Model-behaviour diagnosis Must-have

Weight

What do 1, 3 and 5 look like?

1: Treats every wrong output as a prompt problem and starts rewording.
3: Can distinguish a prompt problem from a data or retrieval problem when the difference is pointed out.
5: Locates the right layer unprompted, and says clearly when no prompt will fix the issue.

Instruction robustness and versioning Can learn

Weight

What do 1, 3 and 5 look like?

1: No version history; can't say what changed between a working version and a broken one.
3: Keeps some record of changes; instructions mostly survive an unusual input.
5: Tracks every version against its measured effect, and writes instructions that hold up under inputs they hadn't anticipated.

Cross-team communication Must-have

Weight

What do 1, 3 and 5 look like?

1: Can't explain a limitation without jargon, or over-promises a fix that doesn't exist.
3: Clear explanations when asked; connects work to the stakeholder's concern with some prompting.
5: Explains limits honestly in the stakeholder's own terms, and pushes back on requests measurement doesn't support.

Safety and misuse awareness Can learn

Weight

What do 1, 3 and 5 look like?

1: Doesn't recognise an embedded-instruction or data-leak risk.
3: Recognises the main risks once pointed out.
5: Spots risks unprompted and designs guardrails before being asked.

Weighted score– / 5

AI as an advantage4D framework

Delegate: Hand AI the drafting of a blank debrief worksheet and agenda from the competency rubric alone. People add the evidence locally; the scores and the decision stay with the panel.

Show the prompt and the checks

Describe: You are helping a hiring manager prepare a debrief for a Prompt Engineer role. Context: I will paste the competency rubric only — the competencies, their weights and their 1, 3 and 5 anchors. Produce: (1) a blank debrief worksheet with one row per competency and empty columns for each interviewer's independent score, the evidence they cite, the spread of scores and an 'evidence gap?' box; (2) a 45-minute agenda: confirm every scorecard is in and reveal all scores at once, discuss the widest spreads using evidence only, review any must-have scored 1, then look at the weighted total and decide (hire, no hire or one targeted follow-up), and record the reasons; and (3) neutral questions the chair can ask to draw out evidence rather than adjectives. Leave every evidence and score cell empty. Do not suggest a score, a rating or a recommendation. Use British English. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the worksheet follows your rubric exactly — same competencies, weights and anchors — and that the agenda keeps independent scores hidden until the reveal.
Diligence: Interviewers fill in the evidence and scores locally, alone, before the debrief, and the panel decides together. Candidate notes and personal data never go into an AI tool; AI never scores, ranks or compares candidates.

H-EvaluateH-Evaluate gives you a report on each candidate's assessment evidence to bring into this debrief.How H-Evaluate helps →

Don't miss: Letting the panel accept a confident account of a fix without checking whether the candidate's own numbers actually support it.

Stage 10 of 13Optional

0 of 2 done

Check references

References and background checks

Who runs it: Recruiter (references) + HR (checks)
Done when: At least two references that test specific competencies, plus only the checks that are lawful and relevant to the role.

Use when:: It is lawful where you hire, and specific evidence from a referee would change the decision.
Skip when:: Local law or company policy rules it out.

Key tool

Ask each referee

1Which prompt or instruction did [candidate] build that your team is still leaning on today?
2What evidence did they usually bring to the table before calling an instruction change a win?
3What was it like for product or support colleagues, specifically, to get answers from them?

AI as an advantage4D framework

Delegate: Hand AI the drafting of reference questions aimed at the competency the panel was least sure about. Choosing referees, making the calls and interpreting the answers stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter write competency-based reference questions for a Prompt Engineer role. Context: I will paste the competency model and the one or two competencies where our evidence is thinnest — no candidate details. Produce: 3–4 questions per competency that ask for specific examples ('tell me about a time…') rather than ratings; an opening question about how the referee worked with the person and for how long; and a closing question about what their next manager could do to help them do their best work. Use British English and inclusive wording. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the questions ask for examples rather than opinions, that they stay on job-related competencies, and that none asks about health, family or other protected matters.
Diligence: References are taken with the candidate's consent and interpreted by people. Referee answers and candidate details never go into an AI tool; AI drafts the questions, people decide.

Don't miss: Accepting a reference's praise for 'good prompts' at face value, without asking what evidence they actually saw that a change had worked.

Framework

Competency-based referencing

Treat references as a last structured conversation about the candidate's work, aimed at the competencies where your evidence is thinnest.

With the candidate's consent, ask for two people who saw their relevant work directly — ideally a former manager and a peer.
Accept suitable alternatives if a manager or peer isn't available, and never penalise a candidate for a former employer's no-reference policy.
Aim your questions at the one or two competencies the debrief was least sure about.
Ask for examples, not ratings. 'Tell me about a time they…' works here too.
Set the timing of each background check by the law and policy that apply.

Why it works: Generic reference questions tend to produce little job-related evidence. Asking for specific examples tied to the role's competencies turns referencing into something you can use.

Core reference questions and checks

Open with: 'How did you work together, and for how long?' — it tells you how much weight the rest can carry.
Ask: 'Tell me about a time something they owned went wrong. What did they do?'
Close every reference with: 'What should their next manager do to help them do their best work?'
Verify only claims that were material to the decision — don't verify a degree the role never required.
Run criminal-record checks only where the role justifies them and local law allows; many roles don't need one.
Schedule formal work-authorisation checks at the legally required time: before employment starts in the UK; for US Form I-9, Section 1 by the first day of paid work and Section 2 within three business days.

Reference questions specific to this role

1.Which prompt or instruction did [candidate] build that your team is still leaning on today?
Listen for: Concrete, lasting work — not general praise.
2.What evidence did they usually bring to the table before calling an instruction change a win?
Listen for: Independent confirmation of measurement discipline.
3.What was it like for product or support colleagues, specifically, to get answers from them?
Listen for: A real example of them explaining a limit clearly, or pushing back on a request in a way that landed well.

Compliance checkpointUS

If a consumer reporting agency provides a background report, the Fair Credit Reporting Act requires a clear, stand-alone written disclosure and the candidate's written authorisation before you order it. Before rejecting someone because of the report, give them a copy and the FCRA summary of rights and a reasonable opportunity to review and dispute it before any adverse action; if you still reject, send a final adverse-action notice. State and local 'fair chance' laws may delay criminal-history questions or checks until after a conditional offer.

Sources: FTC — Background checks: what employers need to know · FTC — Using consumer reports: what employers need to know · checked 2026-09-25

Stage 11 of 13Required

0 of 2 done

Agree the offer

Offer, negotiation and close

Who runs it: Hiring manager + recruiter
Done when: An accepted written offer within the approved band, with a start date.

Key tool

Build the offer around

•The problem: they'll own an instruction layer that real users depend on.
•Budget for experimentation: a monthly model-usage allowance that's theirs to spend.
•Learning: time and budget to keep up as models and techniques change.
•A clear level: the reasoning behind it, and what evidence would move them up.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the offer message built around what the candidate said matters to them. The figure, the approval and the conversation stay with the hiring manager.

Show the prompt and the checks

Describe: You are helping a hiring manager draft an offer message for a Prompt Engineer role. Context: I will paste three general priority categories (for example: learning, flexibility, scope) — not the candidate's words, notes or personal details; the parts of the offer; and the start-date options — without the salary figure. Produce: a warm, plain-English message that links each part of the offer to one of their priorities, says what happens next and when the written offer follows, and invites questions. Use British English and inclusive wording, and leave [brackets] for figures and names. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that nothing promised is outside policy or the approved band, that every claim about the role is true, and that the message reads as personal rather than templated.
Diligence: The hiring manager places the offer in the band using the same pre-set factors for everyone and makes the verbal offer personally. Figures and personal details are filled in by people, never pasted into AI.

Framework

Close plan (motivators → offer → objections)

Build the offer around what the candidate told you they care about, anticipate their objections, and make sure the verbal and written offers match.

List the candidate's top three motivators from your notes at every stage.
Make the verbal offer personally, and link each part of it to a motivator.
Ask what else they are weighing and when they have to decide.
Place the offer in the band using the same pre-set, job-related factors for everyone, and record why.
Send the written offer promptly after the verbal one.

Why it works: A close plan ties the offer to the candidate's stated priorities and makes approvals explicit before the final interview.

Offer checklist and negotiation

Stay inside the approved band. If the candidate is worth more, re-level them with evidence rather than stretching the band.
Trade in things that cost less than salary where policy allows: start date, learning budget, working pattern.
Before the written offer, ask: 'What would make this an easy yes?'
Agree approval and the pay band before the final interview, so the offer isn't held up.
The hiring manager makes the verbal offer; the written offer follows within a working day.
Record which pre-set factors placed the offer where it is in the band.
Tell unsuccessful finalists personally, without avoidable delay, with brief feedback.

What will close this candidate

The problem: they'll own an instruction layer that real users depend on.
Budget for experimentation: a monthly model-usage allowance that's theirs to spend.
Learning: time and budget to keep up as models and techniques change.
A clear level: the reasoning behind it, and what evidence would move them up.

Compliance checkpointEUUS

Use objective, gender-neutral, pre-agreed factors to place candidates within the band, apply them consistently and record the decision as an internal pay-equity control. Check the national law implementing Directive (EU) 2023/970 and any local record-keeping rules; this recommendation is good practice, not a universal statutory documentation rule.

Sources: EU Pay Transparency Directive 2023/970 · checked 2026-09-25

Stage 12 of 13Required

0 of 2 done

Help them settle in

Pre-boarding and 30-60-90 onboarding

Who runs it: Hiring manager + buddy + HR
Done when: A new hire who has worked through a written 30-60-90 plan tied to the 12-month outcomes, with a check-in at each milestone.

Key tool

Before day one
Every account, login and repository permission this role needs, set up before they arrive.
Days 1–30: learn
Meets product, support and engineering colleagues who depend on the instruction layer.
Days 31–60: contribute
Builds or hardens the case set for the highest-risk instruction.
Days 61–90: own
Owns the instruction layer for one area end to end, with a case set running before each change ships.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the 30-60-90 plan from the 12-month outcomes. Agreeing the plan with the new hire stays with the manager.

Show the prompt and the checks

Describe: You are helping a hiring manager draft a 30-60-90 day onboarding plan for a new Prompt Engineer. Context: I will paste the 12-month outcomes from the intake meeting, the systems and people the hire will work with, and anything they will need before day one. Produce: a pre-boarding checklist (equipment, access, a named buddy, a first-week schedule); then three sections — days 1–30 learn, days 31–60 contribute, days 61–90 own — each with 3–4 concrete, checkable expectations and one milestone check-in question. Use British English and plain wording. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each expectation is realistic for a new starter, ties back to a 12-month outcome, and offers the hire support rather than a test.
Diligence: The manager agrees the plan with the new hire in week one and owns the check-ins. The new hire's personal details stay out of the prompt.

Don't miss: Giving a new prompt engineer a large new instruction set to own before they understand how the existing ones are measured.

Framework

30-60-90 day plan

Three phases — learn, contribute, own — with written expectations for each, agreed with the new hire in week one.

Before day one: equipment, access, a named buddy and a first-week schedule.
Days 1–30, learn: systems, people and the current state of the work.
Days 31–60, contribute: a first scoped piece of work shipped with support.
Days 61–90, own: an area of responsibility with clear outcomes.
Hold a check-in at each milestone against the written plan.

Why it works: Hiring doesn't end at the signature. A written onboarding plan makes expectations and support visible to the new hire and their manager.

Before day one
- Every account, login and repository permission this role needs, set up before they arrive.
- A model-provider API key with a sensible spending cap already attached.
- A named buddy from the engineering team.
- A reading list: the current instruction sets, how each is measured, known incidents.
Days 1–30: learn
- Meets product, support and engineering colleagues who depend on the instruction layer.
- Maps every prompt and agent instruction in production and how each is measured today, if at all.
- Ships one small, low-risk instruction fix, to learn the deployment path.
Days 31–60: contribute
- Builds or hardens the case set for the highest-risk instruction.
- Surfaces a specific case where an instruction was quietly failing that nobody had a number for before now.
- Adds version tracking that ties a change to its measured effect.
Days 61–90: own
- Owns the instruction layer for one area end to end, with a case set running before each change ships.
- Ships one improvement backed by their own before-and-after counts.
- Walks the product team through what they found, in language a non-engineer can act on.

Compliance checkpointUSUK

US: the employee completes Form I-9 Section 1 no later than their first day of paid work, and the employer completes Section 2 within three business days after that day (on the first day if the job lasts less than three business days). UK: complete the prescribed right-to-work check before employment starts, and apply it the same way to everyone.

Sources: USCIS — Form I-9 instructions · GOV.UK — Check a job applicant's right to work · checked 2026-09-25

Stage 13 of 13Required

0 of 3 done

Measure time to hire

Measure the process: mean time to hire

Who runs it: Recruiter / talent lead + hiring manager
Done when: A stage-by-stage timing record for this hire, the slowest step named, and one change agreed before the next requisition opens.

Key tool

Record when the hire accepts

•Time to hire: days from the successful candidate entering the pipeline (application or first contact) to the accepted offer. Average it across hires for mean time to hire.
•Time to fill, recorded separately: days from requisition approval to the accepted offer.
•Days per stage, from the stage's first action to its last — and whether the time was spent waiting or working.
•Offer-acceptance rate: offers accepted divided by offers made.
•Where candidates dropped out, and the reason you were given.
•Days the pay band took to get final approval, kept separate from the days spent interviewing: pre-approval days count towards neither measure, post-approval days towards time to fill.

Check in at 90 days whether the hire is meeting the outcomes in the job description — that tells you whether the process picked well.

Read: mean time to hire Read: time to hire vs time to fill

AI as an advantageAI boost4D framework

Delegate: Hand AI the job of building a blank timing worksheet and the instructions for reading it. Entering dates, working out the timings and deciding what to change stay with the recruiter and the hiring manager.

Show the prompt and the checks

Describe: You are helping a recruiter and a hiring manager set up a timing review for a Prompt Engineer hire. Context: I will list the stage names we used, in order (for example requisition approved, sourcing, screening, recruiter call, work sample, interviews, debrief, references, offer accepted). Produce: (1) a blank worksheet with one row per stage and columns for start date, end date, days in stage, days spent waiting (scheduling, approvals, no reply) and number of candidates who entered and left; (2) step-by-step instructions for us to work out, from our own records, time to hire (the successful candidate's entry to the pipeline, by application or first contact, to the accepted offer; average it across hires for mean time to hire) and, separately, time to fill (requisition approval to the accepted offer); (3) five questions we should ask about the slowest stage, separating waiting from working; (4) a short template for recording one change to make before the next hire. Use British English. Do not include benchmarks or example figures. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the worksheet covers every stage you actually ran, that the two definitions are not mixed up, and that the questions point at the process rather than at any person.
Diligence: People fill in the dates and do the arithmetic from their own records, choose the one thing to fix and write it down. No dates, names or details about any candidate go into the prompt.

Don't miss: Treating a slow pay-band approval as a sourcing problem and widening the search, when the actual delay sat in a title and levelling disagreement.

Framework

Mean time to hire, by stage

Time to hire is the number of days from the successful candidate entering your pipeline (their application or your first contact) to their accepted offer; mean time to hire is that figure averaged across hires. Time to fill — requisition approval to acceptance — is a separate measure. Record both per stage, so you can see where the time went rather than only the total.

When the hire accepts, write down the date each stage started and ended, and the day the successful candidate entered the pipeline.
Work out the days per stage, the time to hire and the time to fill, and mark the slowest stage.
Note the offer-acceptance rate and the stage where most candidates dropped out.
Separate waiting time (scheduling, approvals, silence) from working time, and decide which one to shorten.
Fix one thing before the next hire, and write down what you changed.

Why it works: A single total hides where the delay sits. Per-stage timing points at the one step to fix, and the acceptance rate and drop-out points show whether candidates kept pace with the process.

Record for every hire

Time to hire: days from the successful candidate entering the pipeline (application or first contact) to the accepted offer. Average it across hires for mean time to hire.
Time to fill, recorded separately: days from requisition approval to the accepted offer.
Days per stage, from the stage's first action to its last — and whether the time was spent waiting or working.
Offer-acceptance rate: offers accepted divided by offers made.
Where candidates dropped out, and the reason you were given.
Check in at 90 days whether the hire is meeting the outcomes in the job description — that tells you whether the process picked well.

What to log for this role

Days the pay band took to get final approval, kept separate from the days spent interviewing: pre-approval days count towards neither measure, post-approval days towards time to fill.
How long the work-sample marker took to score the 15 cases, given how much of that call is a subjective read rather than a fixed answer.
How many candidates the embedded-instruction line in the work sample caught out, and at what point they would otherwise have passed.

Where this role's hiring slows down

Watch for the pay band stalling on a title disagreement — is this really 'engineering' — rather than on candidate quality.
Watch for two markers scoring the same case-set submission far enough apart that the subjective parts of the brief need a tighter rubric.
Watch for the model-behaviour-and-systems round being rescheduled repeatedly, since it depends on a single staff or principal engineer.

Compliance checkpointUKEU

Under the EU and UK GDPR, tell candidates how long you will keep their data, or the criteria used to decide. Give that notice when you collect data from them directly, or within the Article 14 time limit when it comes from another source (such as a referral or a sourcing tool). Delete or anonymise data when it is no longer needed, keeping only what a documented lawful purpose still requires.

Sources: GDPR — Regulation (EU) 2016/679, Articles 5, 13 and 14 · ICO — UK GDPR guidance · checked 2026-09-25

General information, not legal advice. Rules depend on the employer, the role and where the work is done — confirm what applies with HR or counsel.

Frequently asked questions

Is prompt engineer still a real job title?

It depends on the company. Use a dedicated title when maintaining and evaluating an instruction layer is a sustained body of work with clear ownership. Where that isn't the case, assign the responsibility explicitly within an existing engineering or product role instead of leaving it as an unowned side task.

What's the difference between a prompt engineer and an AI engineer?

An AI engineer builds the surrounding system — retrieval, tool calling, deployment, monitoring. A prompt engineer focuses on the instruction layer itself: the prompts, system messages and agent instructions, plus the case sets that prove they work. In a small team the same person often does both; the dedicated title appears once the instruction layer is big enough to need a full-time owner.

How do you test a prompt engineer in an interview?

Give them a mediocre set of instructions and a handful of cases it fails on, and watch how they work. Look for a clear hypothesis, controlled changes where practical, repeated runs where outputs vary, and honest reporting of uncertainty and regressions. Someone who reaches straight for a rewrite, or can't say how they'd know it worked, is showing you the gap.

Should prompt engineers be allowed to use AI tools in the assessment?

Yes. This role expects you to use AI tools as part of the work, so banning them in an assessment tests something the job doesn't require. State the policy in the brief, ask candidates to say which tools they used, and pay attention to whether they checked the output rather than shipping it as given.

How many interview rounds does a prompt engineer need?

One work sample plus three structured rounds — a work-sample deep-dive, a model-behaviour and systems round, and a hiring-manager round — covers the competencies in this kit. Add a further round only if it tests something the other three genuinely miss; each extra round adds scheduling risk without necessarily adding evidence.

Do we need a dedicated prompt engineer or can an existing engineer own this?

Count the instruction layers you maintain, how often they change, how you test those changes and who does it today. When keeping them reliable is a steady body of work with no clear owner, a dedicated prompt engineer is justified; otherwise assign it explicitly within an engineering or product role.

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