A Guide to Using AI in Talent Acquisition
There will be AI integrated into every Talent Acquisition tool for the foreseeable future, and while pre-built tools solve specific problems, we wanted to create a resource for TA leaders to efficiently use the general LLMs like ChatGPT, Claude, Llama, Grok or whatever other model comes out after we publish this article.
The secret to productive, repeatable AI processes, is clear, structured prompts.
This guide not only walks through some pre-built TA prompts by AI for TA Expert Michael Brown (Ex-head of TA for Unicorns like Snyk, Toast, and Lumafied,) we give you the structure and context to build your own high quality prompts using our starter pack.
Three free engineered prompts for TA Leaders
AI can do more than clean up a job post or draft a polite email. Used well, it helps you uncover warm connections in your network, sharpen how you show up online, and spot risks in resumes before they become problems. The difference between dabbling and leading is simple: you don’t just prompt, you engineer. Outcome, format, context, process. That frame turns AI into real leverage.
Our AI starter park has three prompts, engineered with the Door3 framework, built to create value and teach scalable frameworks.
Beginner: Analyze Your LinkedIn Network | A structured prompt
Mine your existing connections for warm opportunities, priority personas, and engagement plays.
Advanced: LinkedIn Personal Brand Coach & Conversation Strategist | Prompt + YAML code
Transform your post history into a content system that converts.
PRO: Resume Fraud Checker Pro | Agent Creation + Prompt + JSON Code
Spot risk signals and generate targeted interview probes with a custom GPT.
Beginner: Analyze Your LinkedIn Network
This isn’t just a clever LinkedIn exercise—it’s a well-structured prompt that teaches you how to work with AI in a repeatable way. The reason it’s effective comes down to four design choices:
Clear Role for the AI
Instead of saying “help me with LinkedIn,” the prompt frames the AI as an expert in strategic network analysis, opportunity mapping, and relationship-driven growth. This gives it a lens to interpret the data and return insights at the right level.Context About You and Your Goal
By telling the AI your role (e.g., Head of TA) and your objective (hiring, client acquisition, partnerships), the advice becomes tailored rather than generic. Context is what makes the output actionable instead of surface-level.Do’s and Don’ts
The inclusion of ✅ what to do and 🚫 what not to do creates boundaries. This prevents wasted output (like cold outreach spam) and ensures the focus stays on warm, relevant actions.Structured Output Format
By specifying categories—clusters, personas, engagement ideas, top 10 contacts, network gaps—the AI delivers information in a way that’s easy to scan and act on. You avoid the trap of long, unfocused paragraphs and instead get usable deliverables.
How to Replicate This Structure for Other Data
This structure can be copied for almost any data-heavy task you’ll face as a recruiting leader: candidate spreadsheets, interview feedback, headcount plans, or even sourcing lists. The formula is:
Define the AI’s role/expertise (e.g., “You are an expert in recruiter capacity planning and workforce forecasting.”)
Provide context (your role, goals, and what dataset you’re sharing).
Set do’s and don’ts to eliminate noise and guide focus.
Specify the output format so results are organized into buckets you can actually use.
When you follow this framework, AI becomes a scalable analyst. You’re not just getting one-off help—you’re teaching the AI how to think in your terms and returning insights in a format you can rely on.
Advanced: LinkedIn Brand Coach | Prompt + YAML code
YAML works because it turns prompt writing into system design: a schema, a role, a data contract, and a checklist of deliverables. Reuse the scaffold, swap in your dataset and lenses, and you’ve got a repeatable way to turn raw exports into decisions—across LinkedIn, pipelines, interviews, and headcount plans.
This is a strong prompt because it’s designed like a consulting engagement:
Role definition – The AI isn’t just editing—it’s acting as a world-class brand strategist and coach, which sets the lens for deeper analysis.
Explicit inputs – The dataset (
shares.csv) is defined, so the AI knows exactly what to work with.Instructional clarity – It tells the AI how to think (“read like a strategist, not an editor”), which raises the quality of the output.
Framework-driven analysis – Instead of asking for a generic audit, the YAML breaks down categories (voice, patterns, positioning, conversion, blind spots) that structure the output.
Action-oriented outputs – It doesn’t just describe history—it prescribes next steps, giving tactical rewrites, frameworks, and recommendations.
This makes the AI act more like a coach sitting across from you with a whiteboard, not a tool spitting out generic summaries.
How to Reuse This Structure
The genius of this prompt isn’t only what it does for LinkedIn—it’s the architecture. You can apply the same structure to analyze almost any dataset:
Candidate feedback (identify biases, recurring objections, top signals)
Job descriptions (audit voice, clarity, engagement drivers, conversion potential)
Recruiting funnel data (highlight bottlenecks, repeatable strengths, improvement plays)
Employee surveys (map themes, blind spots, action priorities)
The formula stays the same:
Define the AI’s role (e.g., recruiter performance coach, workforce analyst).
Give it clear inputs (CSV, spreadsheet, notes).
Lay out methodology and focus (don’t describe—prescribe; prioritize actionable insights).
Break the output into a framework (voice, patterns, gaps, recommendations).
Why the YAML is good
Schema > wall of text
YAML forces you to declare structure:
name,version,description,input,instructions,analysis_framework,output_specifications,execution_note.That schema acts like an API contract for the model—less ambiguity, more consistent results.
Separation of concerns
What to read →
inputHow to think →
instructions+methodologyWhat to analyze →
analysis_frameworkHow to deliver →
output_specificationsThis keeps the model from guessing and prevents “creative drift.”
Role + constraints = expert mode
You define a role (world-class strategist) and constraints (“don’t summarize—prescribe”), which upgrades the model from editor → consultant.
Explicit data contract
file: "shares.csv",contents: "Historical post data…"tells the model exactly what’s in scope. That reduces hallucinations and improves grounding.
Framework-driven analysis
The
analysis_frameworkbreaks the task into repeatable lenses (voice, patterns, audience, conversion, blind spots). These are reusable modules you can mix/match later.
Action-weighted outputs
output_specificationsand the “coaching, not summarizing” directive bias the model toward decisions, rewrites, and next steps, not pretty prose.
Portable + versioned
version: "2.0"and neutral YAML formatting make it portable across ChatGPT/Claude/Gemini and easy to iterate (diffs are obvious).
Testable + automatable
Because it’s structured, you can:
Keep a golden sample dataset and compare outputs across model versions.
Run it in a batch pipeline (weekly audits, monthly reviews).
That’s what makes this prompt scalable—it doesn’t just solve one problem, it gives you a reusable blueprint for turning raw data into strategy.
PRO: Resume Fraud Checker Pro | Agent Creation + Prompt + JSON Code
This prompt works because it’s a mini product: strict instructions, reusable modes, a JSON contract, domain knowledge, and an action-oriented rubric. Reuse the same blueprint to turn any recruiting dataset—from offers to interviews—into consistent, auditable decisions with next steps.
Why “Resume Fraud Checker Pro” is a great prompt
Systemized, not ad-hoc
You don’t ask the model to “review a resume.” You define a product: name, capabilities, modes, and rules. That reduces randomness and makes results consistent across resumes and users.
Separation of concerns
System Instructions = thinking + behavior
JSON Schema = machine-readable output contract
Knowledge Base = domain context (Success Profiles, Red Flags, baselines)
Prompt Library = task-level entry points (single, batch, comparator, fit)
Clear layers keep the model from improvising and make it easy to maintain.
Opinionated scoring + decisions
Axis scores, overall risk rounding, and Pass / Pause / Decline create a repeatable rubric. This turns fuzzy review into an auditable decision system.
Evidence discipline
“Cite exact lines or bullet fragments” forces traceability. You can defend decisions and coach interviewers with receipts.
Action bias
Every run outputs verification plans and interview probes, not just commentary. You move from “suspicions” to next steps.
Mode architecture (Batch, Comparator, Fit, LinkedIn)
Multiple workflows are supported with one brain. This is scalable: same engine, different entry points.
Hard guardrails
Browsing off, no accusations, crisp tone, normalized dates, PII redaction. These constraints reduce risk and keep output usable in real teams.
Ops-ready troubleshooting
Quick fixes (verbosity, date normalization, missing sections) are documented. You’ve pre-solved the common failure modes.Download the Recruiting AI Starter Pack
Unicorn Talents AI for TA Starter Pack Delivers Results
We’ve made it simple to take the first step. Download the free starter pack and get three proven prompts built for Recruiting Leaders. Use them as they are, or as frameworks to design your own.