Day 12 of 20 · AI for Recruitment

LinkedIn vs Job Spec — Candidate Fit at a Glance

⏱ 7 min 📊 Beginner

On Day 5, you learned how to analyze a single resume against a job description. Today you are taking that to the next level — building a reusable prompt that takes any LinkedIn profile and any job spec and returns a complete fit analysis in seconds.

This is the single most time-saving prompt many recruiters build during this course. Instead of spending 10-15 minutes manually cross-referencing a candidate's experience against a JD, you paste two blocks of text and get a fit score, matching skills, gaps, interview talking points, and potential concerns — instantly.

You will use this for every candidate, every role, from this point forward.

The reusable fit-analysis prompt

Here is the master prompt. Save it somewhere you can access it quickly — your notes app, a pinned ChatGPT conversation, a custom GPT, or a text expander shortcut.

The prompt:

"I'm going to give you a candidate's LinkedIn profile and a job description. Analyze the fit and return the following:

1. Fit Score — A percentage (0-100%) indicating overall match

2. Matching Skills & Experience — Bullet list of where the candidate directly meets the JD requirements

3. Gaps & Missing Qualifications — Bullet list of JD requirements the candidate does not clearly demonstrate

4. Transferable Strengths — Skills or experience that are not exact matches but add value

5. Interview Talking Points — 3-5 specific questions to explore based on the gaps or interesting aspects of their background

6. Potential Concerns — Anything that might be a risk: short tenures, career gaps, overqualification, industry mismatch

7. Overall Recommendation — One sentence: proceed to interview, maybe with reservations, or pass

Here is the LinkedIn profile:

[PASTE LINKEDIN PROFILE TEXT]

Here is the job description:

[PASTE JD]"

That is it. One prompt, two inputs, seven outputs. Every time.

Knowledge Check

What makes the fit-analysis prompt more useful than manually comparing a profile to a JD?

It delivers a structured, consistent analysis in seconds that would take 10-15 minutes manually — and ensures you check the same criteria every time

AI is always more accurate than human judgment

It eliminates the need for interviews

LinkedIn profiles are too long to read manually

The value is not just speed — it is consistency. When you manually skim profiles, you unconsciously weigh different factors for different candidates. The structured prompt forces the same evaluation framework every time, reducing bias and ensuring you do not miss important gaps or strengths.

How to read the fit score

The fit score is a guideline, not a verdict. Here is how to interpret it:

85-100% — Strong match. The candidate hits most or all key requirements. Likely worth a conversation. Focus your interview on culture fit and growth potential rather than qualification verification.

70-84% — Solid match with some gaps. Most candidates worth interviewing fall in this range. The gaps section tells you exactly what to probe in the interview.

50-69% — Partial match. The candidate has relevant experience but is missing meaningful qualifications. Could work if the gaps are trainable or if the transferable strengths are strong.

Below 50% — Weak match. Significant gaps between the profile and the JD. Only proceed if the candidate brings something exceptional that the JD does not capture — a rare network, niche expertise, or unique perspective.

Remember: a 72% fit score with a candidate who is genuinely excited about the role often outperforms a 90% fit score with someone who is passively exploring. The prompt gives you data — your recruiter instinct adds the context.

One prompt, two inputs, seven structured outputs — for every candidate, every role.

Building this as a saved prompt or custom GPT

To get maximum efficiency, save this as a reusable tool you can access in one click:

Option 1 — Pinned conversation: In ChatGPT, start a conversation with the master prompt. Pin it. Every time you need to analyze a candidate, open that conversation and paste the new profile and JD.

Option 2 — Custom GPT: Build a simple custom GPT with the master prompt as its system instructions. Name it something like "Candidate Fit Analyzer." Now you have a dedicated tool in your sidebar — paste in the profile and JD, and it runs the analysis automatically.

Option 3 — Claude Project: Create a Claude Project with the master prompt as the project instructions. Upload the JD as a project file. Then for each candidate, just paste their LinkedIn profile and the analysis runs against the stored JD.

Option 4 — Text expander: If you use a text expander tool (TextExpander, Raycast, or even keyboard shortcuts), save the master prompt as a snippet. Type your trigger, the full prompt appears, and you paste in the details.

Whichever method you choose, the goal is the same: reduce the friction between "I found an interesting candidate" and "I know exactly how they fit this role" to under 60 seconds.

🤖

AI Assistant

online

Here's a LinkedIn profile and a JD. Run the fit analysis. LINKEDIN PROFILE: Jordan Mitchell — Head of Growth Marketing at ScaleUp (Series C, B2B SaaS). 4 years in current role. Previously: Senior Growth Manager at HubSpot (3 years), Marketing Manager at a mid-size agency (2 years). Skills: demand gen, paid media (Google/Meta), ABM, marketing automation (Marketo, HubSpot), SQL for analytics. Led team of 8. Grew pipeline from $12M to $45M ARR. MBA from NYU Stern. JOB DESCRIPTION: VP of Marketing at CloudMetrics (Series B, B2B SaaS, 150 employees). Own full marketing function: demand gen, content, brand, product marketing. Build and lead team of 12. Report to CEO. Requirements: 8+ years marketing experience, 3+ years leadership, B2B SaaS, proven pipeline growth, experience with marketing automation, budget management ($2M+). Nice to have: product marketing experience, international market expansion.

**Candidate Fit Analysis: Jordan Mitchell → VP of Marketing, CloudMetrics** **Fit Score: 78%** **Matching Skills & Experience:** - 9 years total marketing experience (exceeds 8+ requirement) - 4 years leading a team of 8 (meets 3+ years leadership) - Deep B2B SaaS background (HubSpot + ScaleUp) - Proven pipeline growth: $12M → $45M ARR (strong evidence) - Marketing automation expertise (Marketo + HubSpot) - SQL proficiency for data-driven marketing decisions - MBA from a top business school **Gaps & Missing Qualifications:** - No clear content marketing or brand experience — background is heavily growth/demand gen - No product marketing experience mentioned (listed as nice-to-have) - No evidence of international market expansion - Current team is 8 — role requires managing 12; manageable stretch but worth probing - Budget management not explicitly mentioned — unclear if they've owned a $2M+ budget **Transferable Strengths:** - Agency background suggests versatility across marketing disciplines - HubSpot experience means exposure to how best-in-class marketing orgs operate - MBA adds strategic and financial literacy **Interview Talking Points:** 1. "You've led demand gen — but this role also owns content, brand, and product marketing. How would you approach building functions you haven't directly managed?" 2. "Walk me through the largest marketing budget you've managed. How did you allocate across channels?" 3. "The role reports directly to the CEO. Tell me about a time you influenced C-level strategy." 4. "We're exploring European expansion next year. What's your experience with international markets?" 5. "Your team will grow from 8 to 12. What's your approach to hiring for disciplines outside your core expertise?" **Potential Concerns:** - Growth/demand gen specialist may struggle with the breadth of a full-function VP role - No product marketing experience could leave a gap in a critical function - Has not worked at a company as early-stage as Series B recently — may need to adjust to less infrastructure **Overall Recommendation:** Proceed to interview. Strong demand gen and pipeline credentials. Probe depth on brand, content, and product marketing to assess whether they can lead a full-function team, not just a growth team.

↻ Replay conversation

Knowledge Check

A candidate scores 72% on the fit analysis. What should you do?

Skip the interview and move to a higher-scoring candidate

Reject them — they do not meet the requirements

Review the specific gaps, determine if they are trainable or critical, and decide based on the full picture

Ask AI to re-run the analysis to get a higher score

A 72% score means the candidate matches most requirements but has specific gaps. The right move is to read the gaps section carefully. If the gaps are in trainable skills or nice-to-haves, the candidate is absolutely worth interviewing. If the gaps are in core, non-negotiable requirements, that changes the calculus. The score is a starting point for your judgment, not a replacement for it.

Using this for every candidate, every role

The real power of this prompt is not in any single analysis — it is in running it consistently across every candidate you evaluate.

When you use the same framework for every candidate on a role, three things happen:

Pattern recognition — After analyzing five or six candidates, you start seeing clear patterns. Maybe the market has plenty of candidates with skills A and B, but very few with skill C. That insight helps you advise hiring managers on what is realistic to expect.

Faster decisions — Instead of re-reading profiles three times trying to remember what you noticed, you have a structured summary you can reference in seconds. Your shortlist comes together in minutes, not hours.

Better hiring manager conversations — Instead of saying "I think this candidate is strong," you can say "They scored 81%. Strengths are X, Y, Z. Gaps are A and B. Here are the questions I'd ask to probe those gaps." That is a recruiter who gets taken seriously.

Final Check

What is the biggest long-term benefit of running the fit analysis on every candidate consistently?

It guarantees you will hire the best person

It creates pattern recognition across candidates, speeds up decisions, and gives you data-backed insights for hiring manager conversations

It replaces the need for interviewing

It saves you from reading LinkedIn profiles

Consistency is the multiplier. One analysis saves you 10 minutes. Running it on every candidate for a role gives you a data set — you can compare scores, spot market trends, and advise hiring managers with real evidence. That is the difference between a recruiter who processes applications and one who provides strategic talent intelligence.

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Day 12 Complete

"One prompt. Two inputs. Seven structured outputs. Save the fit-analysis prompt somewhere you can reach it in one click — you will use it for every candidate from now on."

Tomorrow — Day 13

Candidate Assessment & Comparison

Tomorrow you'll learn to create side-by-side candidate comparisons that make hiring decisions clearer and faster.

Continue to Day 13 →