This tutorial starts with DINQ already connected. Read the connection guide first
Write one prompt that spells out the mapping
Results mostly come down to whether you clearly stated the role, scope, fields, and standard. Just copy the template below and fill in the placeholders.
Ready-to-copy prompt template
Fill in the placeholders in the brackets and send it to your AI workspace. Start with a 10–20 person sample the first time, and scale up once the format looks right.
Using the connected DINQ tool, help me build a headhunter-grade talent mapping that meets the delivery standard of the top-5 international executive search firms. [Role background] - Target role: (e.g. Quant Researcher / Autonomous Driving Algorithm Engineer / IR Director) - Location: (e.g. Hong Kong / Beijing & Shanghai / US, bilingual Chinese background preferred) - Target companies: (list 5–15 target companies; search them one at a time) [Required fields] Each candidate must include: 1. Name (Chinese and English, if applicable) 2. Current company + title + city 3. Full LinkedIn URL (must be a real, clickable personal profile link in the format https://www.linkedin.com/in/xxx/ — mark "-" if not found, never fabricate) 4. Email (state status: verified / inferred from company format, unverified / none — suggest InMail) 5. Personal site / GitHub / Google Scholar (exhaustively search these for technical or academic backgrounds) 6. Education and years of experience 7. Tier: Tier A (strong match, write a rationale for each person) / Tier B / Tier C [Data quality requirements] - Cross-check DINQ's results with a web search: verify whether the candidate has switched jobs, whether the role is stale, and whether the location matches; - Flag any name confusion, outdated info, or candidates clearly outside scope; - Mark any missing data as "-" — never leave it blank, and never fabricate. [Delivery format] (e.g. a bilingual Chinese/English PowerPoint deck with a dark, premium color scheme; or start with a table I can review first)
Advanced: export your raw data first for better results
If you've already shortlisted a batch of candidates in DINQ, we strongly recommend this workflow:
① Export from DINQ
Export your shortlisted candidates from DINQ as a CSV / spreadsheet file (with a LinkedIn link column).
② Upload it to your AI workspace
Drag the exported file straight into your AI workspace. This file becomes the "source of truth" for the roster and LinkedIn links.
③ Have your AI workspace enrich it
In your prompt, tell your AI workspace to "treat the uploaded file as the source of truth — use DINQ and web search to correct errors, fill in contact info, update roles, and tier the candidates."
3 hard standards for a solid mapping
Check against these before delivery — if it falls short, have your AI workspace keep revising.
Top-5 headhunter delivery quality
Complete structure: overview, talent distribution, tiering framework, individual profiles, outreach suggestions. Professional visuals, ready for the client as-is.
Exhaustive profiles and contact info
LinkedIn, email, GitHub, and Google Scholar searched one by one; anything not found is honestly marked — never fabricated.
Correct · Enrich · Process
Use your AI workspace to cross-check whether roles are stale or people have moved on, fill in background info, and turn the raw data into a finished, tiered, judgment-informed product.
5 common pitfalls to avoid
Searching too many companies at once
Break "search 10 companies for candidates" into one company at a time — DINQ's results are noticeably better that way.
Search takes a bit of time — don't interrupt it
Let the current search finish before retrying. If a call fails or returns partial results, ask your AI workspace to explain the status and check what is missing before repeating it.
Mass-emailing inferred addresses as if verified
Email coverage is probabilistic. Addresses marked "inferred, unverified" must be verified before sending, or you'll hurt deliverability and brand reputation.
Delivering without checking LinkedIn links
Spot-check a few links actually open before delivery. Requiring your AI workspace to use only real links and mark missing ones with "-" is a hard rule to put in the prompt.
Ignoring Credits usage
Check your personal DINQ credit balance before searching across multiple roles. Partial results with candidates can be billable; see the connection guide for billing rules.