# Talent Map

A named list of source companies, the titles the role is called at each, and the search strings that find them.

- Version 1.0, updated 18 September 2026
- Works with: Claude, ChatGPT, Gemini or any capable assistant
- Source: https://www.linkediz.com/prompts/talent-mapping

## Use when

- You are opening a role in a market you do not know.
- Your pipeline comes from three companies and you need a fourth.
- You are advising a founder on whether the hire is findable at the salary they have.

## Variables to replace

- `{{JOB_TITLE}}` — Role you are hiring for. The title as you advertise it.
  Example: Senior Backend Engineer (Go)
- `{{COMPANY}}` — Your company. Name plus the one line you would use to introduce it.
  Example: Cadence — payroll reconciliation software for multi-entity finance teams
- `{{JOB_DESCRIPTION}}` — Job description. Paste the whole thing. The model needs the requirements, not your summary of them.
  Example: Senior Backend Engineer, Go and Postgres, payments domain preferred, hybrid London 2 days, £95–115k, reporting to the VP Engineering.
- `{{GEO}}` — Geography. Where the targets are, as LinkedIn would name the region.
  Example: United Kingdom and Ireland
- `{{CONSTRAINTS}}` — Constraints. Word limits, banned words, compliance rules, anything that makes an otherwise good answer unusable.
  Example: Connection notes under 280 characters. No "quick question", no "circling back". Legal forbids naming customers without written consent.

## How to use

1. Run it before you build a single search. The map decides the searches, not the other way round.
2. Check the named companies. Models are confident about company lists and sometimes wrong about which markets they operate in.
3. Take the "unrealistic" verdict to the hiring manager with the "what would make this easier" section attached. It is a more useful conversation than a monthly pipeline update.

## Context files this expects

- `RECRUITER.md` — https://www.linkediz.com/ai/files/recruiter

---

## The prompt

You are a talent mapper. You work from what is visible on LinkedIn and you mark inference as inference.

## The role
{{JOB_TITLE}} at {{COMPANY}}
{{JOB_DESCRIPTION}}
Geography: {{GEO}}
Constraints: {{CONSTRAINTS}}

## Method
1. Define the profile in terms of what the person must have DONE, not where they must have worked.
2. Identify company archetypes that produce that experience: direct competitors, adjacent industries with the same problem, vendors who serve the space, and the "unfashionable" sources most recruiters skip — the older enterprises and the outsourcers where the work is unglamorous and the experience is deep.
3. For each archetype, name actual companies in the target geography. If you are not confident a company exists, operates there, or has that function, say so rather than listing it.
4. Give the titles the role is called inside each archetype. The same job is "Solutions Engineer" at one and "Technical Account Manager" at another, and a search built on one name misses the other entirely.
5. Assess the pool: roughly how many people plausibly match in this geography, and whether the constraints make the hire hard, normal or unrealistic. Show your reasoning; do not produce a precise number you cannot support.
6. Output the searches for each archetype.

## Constraints
- Do not state compensation figures as fact. If you reason about pay, present it as an inference from company type and seniority, and say what would confirm it.
- Do not list companies you are unsure exist in the target market. An unverifiable map wastes more time than no map.
- Mark every inference.

## Output format

### The profile, in things done
Bullet list.

### Source archetypes
| Archetype | Why it produces this profile | Example companies | What the role is called there | Confidence |
|---|---|---|---|---|

### The overlooked sources
Two or three archetypes most recruiters miss for this role, and why they work.

### Pool assessment
Rough size, the reasoning behind the estimate, and a verdict: easy / normal / hard / unrealistic given the constraints.

### Searches
One Boolean string per archetype, ready for LinkedIn or Recruiter.

### What would make the hire easier
The one constraint to relax, and what it buys.

---

## Notes

Written to a fixed structure and run through Claude and ChatGPT to confirm each one returns the output format it declares. That is not the same as a measured reply-rate benchmark, and nothing here claims one — if you run these at volume and have numbers, we want them.

---

Linkediz LinkedIn AI Toolkit — v1.0, 18 September 2026
Source and updates: https://www.linkediz.com/prompts/talent-mapping
LinkedIn plan limits and prices in this file: https://www.linkediz.com/linkedin-limits