Overview

Lecture URL: https://skill.re/learn/recruiting/prompting-for-resume-screening-sourcing-and-research.php

TRANSCRIPT: Prompting for Resume Screening, Sourcing, and Research

Course: AI for Recruiters - Professional Credential

Module: Level 2: Hands-On Foundations

Section: Chapter 6 -- Effective Prompting for Recruiting Tasks

Theme: effective-prompting-for-recruiting-tasks

Lecture: 6.2

Duration: 60 min

Format: Workshop + Hands-On

Audience: Recruiters beginning to use AI tools

Prerequisites: L1 Certification

What you will learn: You'll master recruiting-specific prompts for analyzing resumes, identifying

fit signals, sourcing candidates, and conducting research. By the end, you'll be able to extract

relevant qualifications, surface hidden strengths, and support faster screening decisions with AI.

INTRODUCTION

Resume screening is one of the highest-volume recruiting tasks. You're looking at dozens of

applications per role, and you need to quickly separate "not a fit" from "might be interesting"

from "strong contender." This is where prompting really shines -- and where prompting poorly

wastes a lot of time.

Today, we're building prompts that transform how you screen resumes. Instead of asking AI for a

generic summary, you'll ask it to extract specific qualifications, flag red flags without bias,

surface cultural fit signals, and highlight what's actually relevant to your hire. By the end,

you'll have a library of screening prompts that save you hours every week.

THE RESUME SCREENING PROMPT FRAMEWORK

An effective resume screening prompt has five layers: role specification, qualification

extraction, red flag identification, cultural fit signals, and decision support.

LAYER 1: ROLE SPECIFICATION

Be brutally specific about what you're hiring for. The job title is not enough.

Weak: "Screen this resume for a product manager role."

Strong: "Screen this resume for a senior product manager role at a B2B SaaS company selling to

healthcare organizations. This person will lead the roadmap for our compliance and integration

products, working with a team of 2 engineers and supporting 15 mid-market customers. Key success

metrics: ability to manage ambiguity, translate customer needs into product decisions, and drive

execution with a small team."

That second prompt tells the AI exactly what "good" looks like. It's not just about PM skills

in the abstract -- it's about skills relevant to your specific situation.

LAYER 2: QUALIFICATION EXTRACTION

Tell the AI which qualifications matter most and how to present them. Don't leave this to chance.

Example: "Extract and list: (1) Years of B2B SaaS experience, (2) Specific industries or customer

segments they've worked with, (3) Team size they've led or influenced, (4) Experience with product

analytics or data-driven decision-making, (5) Evidence of cross-functional collaboration with

engineering and design. For each qualification, note whether it's explicitly stated or inferred

This is much more useful than a generic summary. You get structured information you can compare

across candidates.

LAYER 3: RED FLAG IDENTIFICATION

Red flags exist, but they're often not about discrimination -- they're about role fit. An AI might

flag "worked at 5 companies in 6 years" which could indicate: opportunity-seeking (positive),

unfitness (negative), or external circumstances (neutral). Help it distinguish.

Example: "Identify potential concerns in this resume. For each concern, explain: (1) What is it?

(2) What might cause it (multiple explanations OK)? (3) Would this be a concern for this specific

role (senior PM at a growing SaaS company)? (4) What would you want to ask the candidate to

understand this better?"

The fourth layer -- asking what you'd want to understand -- transforms red flags from filters into

conversation starters. That's much stronger screening.

LAYER 4: CULTURAL FIT SIGNALS

Culture fit is real, and it's also a vector for bias. Good prompts help you see signals without

making unfair assumptions.

Example: "Does anything in this resume suggest: (1) Comfort with ambiguity and change? (2)

Willingness to wear multiple hats? (3) Communication skills and ability to work cross-functionally?

(4) Evidence of learning and growth mindset? For each signal, point to specific examples from the

resume. Do not make assumptions about personal characteristics, background, or identity. Do not

infer cultural fit from educational choices, geographic location, or demographic signals."

That last constraint is crucial. It prevents AI from using implicit bias to "assess" culture fit.

LAYER 5: DECISION SUPPORT

End your prompt with a clear decision framework. Don't ask AI to decide -- ask it to prepare you

to decide.

Example: "Based on the above, provide a one-line summary of this candidate's fit for this role.

Use one of these: (1) Strong fit -- clear qualifications, no red flags, (2) Possible fit -- meets

most criteria, some concerns to explore, (3) Weak fit -- missing key qualifications or significant

concerns, (4) Unable to assess -- insufficient information."

Notice you're not asking "Should we hire this person?" You're asking "What's their likely fit?"

That's a much clearer, less biased output.

BUILDING A COMPLETE RESUME SCREENING PROMPT

Let's build a full resume screening prompt for a data engineer role.

"You are screening resumes for a data engineer role at a mid-market analytics platform. Our team

is 3 people. The role requires: infrastructure knowledge (data warehousing, ETL pipelines, SQL),

hands-on engineering (Python or similar), and ability to take ownership of projects independently.

Please analyze the resume for: (1) Years of data engineering or related experience, (2) Specific

tools and technologies they've used (databases, pipeline tools, cloud platforms), (3) Evidence

of building or maintaining production systems, (4) Ability to work independently vs. in large teams.

Red flags to identify: (1) Long gaps or very frequent job changes, (2) Mismatch between job title

and actual responsibilities, (3) Technology stack that's significantly different from what we use

(note what we use: Snowflake, Python, Airflow, AWS). For each red flag, suggest questions to ask.

Cultural fit signals: (1) Evidence of taking initiative and ownership, (2) Ability to learn new

tools quickly, (3) Communication skills. Point to specific examples.

Constraints:

• Do not make assumptions about the candidate's background, identity, or demographics.
  - Do not penalize educational choices, geographic location, or career paths that are simply different

from a 'typical' trajectory.

• If information is missing, note it explicitly rather than inferring.
  - Format as a structured list with sections for Qualifications, Red Flags, Cultural Fit Signals, and

Overall Fit Assessment."

That's a complete, structured prompt. You'll get back organized information that supports your

decision-making.

SOURCING RESEARCH PROMPTS

Sourcing is detective work. You find a candidate online, and you want to quickly understand: What

have they actually done? What are they interested in? Are they likely to engage?

Example sourcing prompt:

"I found a candidate, Jamal Hassan, on GitHub with a profile suggesting data engineering work.

Research and summarize: (1) Public repositories and their purpose, (2) Technologies he works with

most frequently, (3) Evidence of open source contributions or mentoring, (4) Any indication of his

current role or company (if public), (5) Geographic location if public. Only use publicly available

information. Do not speculate about private details. Keep your answer to 200 words."

This research prompt gets you oriented quickly. You'll know: Does this person really do data

engineering or is the profile misleading? Are they active in open source? Do they seem interested

in learning new tools? That informs whether and how you reach out.

COMPARATIVE PROMPTS: SCREENING MULTIPLE CANDIDATES

When you're comparing candidates, AI can help you see patterns across resumes.

Example: "I'm attaching three resumes. Please compare them on: (1) Years of relevant experience,

(2) Depth of technical skills (extract specific tools and frameworks for each), (3) Evidence of

leading projects or teams, (4) Communication skills and clarity of describing impact. Create a

comparison table. At the end, note: Which candidate seems most ready for a senior role? Which

would benefit most from mentorship? Which has the most diverse skill set?"

Comparative prompts help you calibrate. You see not just who's qualified, but who's qualified

relative to the others.

ANTI-PATTERNS

ANTI-PATTERN 1: SCREENING WITHOUT CONTEXT

Description: Asking AI to screen a resume without explaining what the role actually requires.

Example: "Screen this resume."

Why it fails: The AI has no idea what "good" looks like. You'll get a generic summary that might

highlight irrelevant skills and miss what's actually important.

How to avoid: Always begin your screening prompt with a detailed description of the role, the team,

and what success looks like in the first 90 days.

ANTI-PATTERN 2: ASKING AI TO MAKE THE HIRING DECISION

Description: Ending your screening prompt with "Should we interview this person?"

Example: "Based on all the above, do you think we should interview this candidate? Yes or no."

Why it fails: You're asking AI to make a human decision. It should support your decision-making,

not replace it. You own the hiring decision.

How to avoid: Ask AI to organize information: "What's this candidate's fit assessment?" or "What

questions would you recommend asking?" Then you decide whether to interview.

ANTI-PATTERN 3: BIASED RED FLAG IDENTIFICATION

Description: Using red flag language that's code for discrimination.

Example: "Are there any warning signs that this person might not be committed? Look for signs they

might be looking for work-life balance or that they might leave for personal reasons."

Why it fails: "Looking for work-life balance" and "might leave for personal reasons" often encode

assumptions about parenthood, caregiving, health, or other protected characteristics. This is

discriminatory.

How to avoid: Focus on role-specific concerns: gaps in relevant skills, misalignment with the tech

stack, or short tenures in similar roles that suggest the role wasn't a fit. Always require

specificity and role-relevance.

PRACTICE PROMPTS

Exercise 1: Build a Role-Specific Screening Prompt

Write a resume screening prompt for a specific role you're currently hiring for. Include all five

layers: role specification, qualification extraction, red flag identification (role-specific, not

discriminatory), cultural fit signals, and decision support.

Exercise 2: Comparative Analysis

Take two contrasting resumes (one strong fit, one weak fit for a role you know). Write a prompt

that compares them and identifies what makes one stronger than the other.

Exercise 3: Sourcing Research

You found a candidate on LinkedIn. Write a prompt that tells AI to research and summarize: (1)

their actual experience based on publicly available information, (2) the technologies they're most

skilled with, (3) any indication they're open to new opportunities. What would you want to know

before reaching out?

Exercise 4: Identify Bias in a Screening Prompt

Review this prompt: "Screen this resume for a marketing manager role. Look for red flags like job

hopping, gaps in employment, or anything that suggests they might not be serious about a long-term

commitment. Any sign they might want flexible hours is a concern." What's biased about this? Rewrite

it to be more fair and role-specific.

Exercise 5: Test and Iterate

Write a screening prompt, use it on a real resume, review the output, and identify what would make

it more useful for your decision-making. What questions did the output not answer?

KEY TAKEAWAYS

1. Resume screening prompts need context about the specific role, the team, and what success looks

like. Never ask AI to screen without explaining what "good" means for your situation.

1. Structure your screening prompt in five layers: role specification, qualification extraction, red

flag identification, cultural fit signals, and decision support. This gives you organized information

that supports faster, better decisions.

1. Red flags should be role-specific and based on actual job-fit concerns, not coded discrimination.

Always require AI to explain red flags and suggest follow-up questions rather than treating them as

filters.

1. Ask AI to support your decision-making, not replace it. "What's the fit assessment?" is better

than "Should we hire this person?"

1. Use constraints to prevent bias in sourcing and research. Tell AI what NOT to do: don't infer

motivation from demographics, don't make assumptions about identity, don't penalize non-traditional

paths.

1. Comparative prompts help you calibrate. Screening multiple candidates together gives you a sense

of relative fit and what you're actually looking for.

GLOSSARY

Red Flag: A pattern or piece of information in a candidate's background that might indicate they're

not a fit for the specific role. Role-specific red flags focus on actual job fit, not discrimination.

Sourcing: The process of finding candidates through research, networking, or outreach. Effective

sourcing involves understanding what candidates are actually looking for and what skills they have.

Qualification Extraction: The process of identifying and listing the specific skills, experience,

and achievements from a candidate's materials. AI can help organize qualifications in a structured

way that supports comparison.

Cultural Fit Signal: Evidence from a candidate's background that suggests they'd thrive in your

specific team and company culture. Should be based on observable behaviors and outcomes, not

assumptions about identity or background.

Comparative Analysis: Reviewing multiple resumes side-by-side to understand relative fit and to

calibrate what you're looking for in candidates.

SYNTHESIS AND APPLICATION

Resume screening is one of the highest-leverage places to use AI in recruiting. Every hour you save

on screening is an hour you can spend on relationship-building, interviewing, and closing great

candidates. But the leverage only works if you're using prompts that actually extract what you need

to know.

This week, I want you to take a role you're currently hiring for and build a screening prompt using

the five-layer framework. Test it on 3-5 resumes. Notice what information you're actually using to

make screening decisions and what information the prompt is providing. Iterate. Over time, you'll

develop screening prompts that feel like they were written specifically for your needs -- because they

are.

REFLECTION EXERCISE

1. What do you currently spend the most time on when screening resumes? What information are you

looking for that you never find because you're not specifically asking for it?

1. What red flags have you historically used in screening? Are they role-specific and job-focused,

or do they encode assumptions about identity or non-traditional career paths? How might you reframe

them?

1. Think about a recent hire who worked out really well. What did their resume show you that made

you want to interview them? What signals were you looking for?

1. When comparing candidates, what framework do you use to decide who's "stronger"? How would that

look as a structured prompt?

CLOSING REMARKS

Strong screening prompts turn resume review from a chore into structured intelligence gathering.

In the next session, we'll apply these skills to iterating with AI -- asking follow-up questions,

refining outputs, and getting progressively better results.

AI for Recruiters Certification Program

Level 2: Hands-On Foundations | Effective Prompting for Recruiting Tasks | Lecture 6.2

A SkillsClinic initiative.

Duration: ~60 minutes | Word Count: ~2,420