Prep the Hiring-Manager Intake: The Recruiter's Checklist Playbook

It's 9:08 AM. Your hiring manager intake call is at 10:00. You opened the ATS expecting maybe 40 applicants for the Senior Backend Engineer role. There are 217. The post has been live for 36 hours.

The JD has six must-haves and forty-three nice-to-haves, half of which were added by the hiring manager last week in a flurry of "oh, and ideally..." Slack messages. The scorecard is a Google Doc that has been edited by four people and has a comment thread you have not read.

You have not yet looked at a single resume. The hiring manager is going to walk into the call asking "so, what does the pipeline look like?" — which means in the next 52 minutes you need to: skim 217 resumes, generate a sense of who's actually qualified, identify the top 8-10 to discuss in the intake, and prep an agenda for a productive intake instead of a vibes session.

Every recruiter has had this Wednesday. The math of recruiting — too many applicants, too little time, too few signals on a one-page resume — is brutal. The temptation to offload it to an algorithm is strong.

Here is what actually works, including the part where you don't offload the hiring decision to an algorithm.

First: the line you don't cross

Stop. Before any tactic.

AI is a productivity tool for the parts of recruiting that involve reading, summarizing, drafting, and prepping. It is not a hiring decision system. Do not use any tool — CorpGPT included — for any of these:

• No auto-rejecting candidates on a model score.
• No ranking candidates by a model's "fit score".
• No filtering resumes on inferred demographic signals.
• No candidate decision made by a model — a human reads every resume and decides.
• Don't summarize, compare or shortlist candidates from their resumes in CorpGPT — its Terms rule this out: its output can't be used for hiring decisions about a person.

That's not just ethics. It's a growing list of legal requirements — NYC AEDT, Illinois AIVI, the EU AI Act, parts of the EEOC's emerging guidance — that hold employers accountable for automated decisions. And, more practically: the algorithm doesn't know what the role actually needs. You do, after the intake call. The hiring manager does, after the loop.

The tool's job is the JD, the scorecard, the intake prep and the drafting. The resumes and the judgment are yours. Always.

With that in mind, here is what actually works.

The move: prep everything around the stack, then read it yourself

The mistake most recruiters make in 217-applicant queues is one of two extremes. Either they read 30 resumes carefully, get tired, and make worse and worse skim-decisions on the back half. Or they keyword-search for buzzwords and miss the candidates whose resumes are written in different language but match the spirit of the role.

Stop keyword-searching. Turn the JD and the scorecard into a short reading checklist first, so every resume gets the same questions in the same order. Then you read the resumes yourself, you decide who moves forward, and you spend the time on the calls.

The playbook

Step 1. Upload the JD and the scorecard

Open CorpGPT. Drop in:

• The JD, with the must-haves explicitly called out (e.g., "must-have: 5+ years backend in Go OR Java; must-have: AWS production experience; must-have: located in EST/CST time zone").
• The scorecard (interview rubric).
• Any prior calibration notes from similar roles your team has hired.
• Not the resumes. They stay in your ATS. CorpGPT's Terms rule out using its output for hiring decisions about a person, so candidates' files don't belong in it.

The role's paperwork lands in one searchable place, in a collection for the role in My Documents.

Step 2. Generate the intake prep first

Before you touch a single resume, prep for the intake. From the JD:

• Make an intake call agenda in the Make pane (Meeting Agenda) — discovery questions, calibration questions ("show me a profile you'd say yes to and one you'd say no to and tell me why"), sourcing strategy questions, timeline expectations.
• Ask Chat to draft a must-have refinement worksheet — six must-haves with the open question "which of these are truly disqualifying vs. nice-to-have?" One of the goals of the intake is to actually get to six real must-haves, not "a list everyone signed off on without reading."

Now you're ready to walk into the intake leading the conversation, not reacting to it.

Step 3. Read the pile with a checklist

Now the resumes — in your ATS, not in CorpGPT. First, open Chat with the role's collection ticked in the Sources pane — you can ask questions across the JD, the scorecard and the calibration notes together — and ask for a reading checklist:

• "From the JD and the scorecard, give me a one-page reading checklist: the six must-haves as questions I can answer yes / no / unclear from a resume, and what counts as evidence for each."

You get a checklist grounded in your own JD and scorecard, with the documents it came from cited. No candidate goes into the tool, so there's no ranking, no score, no recommendation — just the same questions for every resume.

Now you read the resumes, checklist beside you. It keeps the read fast and consistent — the same six questions for every candidate. Who moves forward is your call — you never delegate it to a model.

For the intake, ask Chat for an empty write-up template built from the scorecard — relevant experience, gaps to probe, projects worth a follow-up question — and fill it in yourself for the 8-10 people you want to discuss with the hiring manager.

Step 4. Final intake prep

You now have:

• An intake agenda.
• A must-have-refinement worksheet.
• 8-10 write-ups you filled in from your own read.
• Specific calibration data from the actual pipeline ("here are three profiles that are clearly strong on must-haves 1-3 but lighter on 4-6 — is that a yes or a no for you?").

This intake is going to be different. Instead of "so, what does the role look like?" you're going to walk in with three real candidates and force a calibrating decision. The hiring manager leaves the call knowing what they actually want, because they had to react to specific people.

Then: the intake call

Hiring manager: "So, what does the pipeline look like?"

You: "I've got 217 applicants from 36 hours of posting. I pulled the 10 most relevant — let me walk you through three of them, see what you think, and then we'll firm up the must-haves."

By the end of the call, the must-haves are clearer than they've been all month. You go back to the desk with directional clarity, not vibes.

Beyond the one role: the recruiter's library

Build a per-role collection

Every active req gets its own collection in My Documents: JD, scorecard, intake notes, sourcing strategy, calibration notes — candidate materials stay in your ATS. Six weeks in, when you onboard a co-recruiter or take leave, you can brief them straight from the collection — ask Chat for the req's history and send them the answer. The Copy button copies the answer's words without its sources, so name the documents it came from yourself.

Use Record audio for your own debrief notes

Right after a round of calls, talk through what you learned about the search itself — the questions that worked, what the market is telling you, what to change in the JD. Record audio uses your device's microphone, so it records you, not the call. Keep your notes on individual candidates in your ATS: CorpGPT's Terms rule out using its output for hiring decisions about a person.

Use My Courses to ramp on a new function

Staffed on a sales-engineering role and you've never recruited one before? Drop two role-specific articles, an SE leveling guide, and a few public job posts for similar roles into a collection. Build a short course from them in My Courses — eight steps per module, from a summary to a quiz. You walk into your first sourcing session knowing the difference between a true SE and a "demo engineer" — without having to bug the hiring manager three times.

Generate outreach drafts, not outreach spam

Ask Chat to draft outreach messages from the role's pitch — three-sentence opener, one-line value prop, and a slot for the one specific reason you're writing to this person. You add that reason, edit, send. Faster than blank-page sourcing. Less robotic than templated InMail. Always your voice on the final send.

The features doing the work

Chat — reading checklists and write-up templates from your JD and scorecard, the must-have worksheet, intake prep and outreach drafts. Resumes stay in your ATS.

Make pane — intake meeting agendas, summaries, action items, study guides, built from the documents you uploaded.

Chat across everything — find any prior intake note, calibration note, or sourcing memo across your library by describing what you're looking for.

My Courses — a structured ramp on a new function or a new technical area before a hiring manager intake, built from your own documents.

Record audio — your own spoken debrief notes after a round of calls (it records your microphone, not the call), transcribed and searchable.

Why this actually works

Three forces are doing the real work.

First, recruiting is mostly reading and writing. The reading — resumes, JDs, calibration notes, scorecards — eats your morning. The writing — write-ups, outreach, intake prep — eats your afternoon. Everything except the candidates themselves can be drafted fast, grounded in your own documents. What's left is the part of the job that actually requires you: the conversations, the judgment, the relationships, the sell.

Second, a shared checklist makes your read consistent. When every resume gets the same six questions in the same order, your calls are easier to explain — to the hiring manager, to the candidate, and to anyone who audits the process later.

Third, the line between summarization and decision-making stays clear. The tool never sees the candidates. The recruiter decides. That's defensible — to the candidate, the hiring manager, the legal team, and the regulator.

What this can't do — and shouldn't

Be honest about this. Recruiting is one of the highest-stakes places to misuse AI.

CorpGPT does not make hiring decisions. It does not score candidates. It does not auto-reject. It does not infer demographics. It does not predict who will succeed in a role. Anyone selling you a tool that does any of these things is selling you a lawsuit.

What it does is the JD work, the drafting, and the prep. The grunt work. The interviewer judgment, the cultural read, the close, the offer negotiation, the candidate experience — that's still you. The hiring decision is yours and the hiring team's, with the recruiter accountable for the process and the hiring manager accountable for the call.

Document the process. Use the same checklist for every candidate. Train your team. Stay current on jurisdictional requirements. Talk to your employment counsel before any workflow change involving AI and candidates.

The bottom line

A full pipeline. One role. A hiring manager waiting on the intake. Good luck — and you'll need less of it.

Checklist built. Write-up template ready. Intake agenda drafted. Calibration questions prepped. Time saved goes back into the conversations only you can have.

Find the needle. Run the loop. Hit the quota. The wizard is the recruiter doing the human parts of the job at full attention, because the reading-and-writing parts finally take less of the day.

Open CorpGPT. Upload the JD and the scorecard. Read the pile yourself. Walk into the intake.

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