Day 5 of 20 · AI for Recruitment

Reading a LinkedIn Profile in 30 Seconds

⏱ 7 min 📊 Beginner

This is one of the most practical lessons in the entire course. It solves a problem every recruiter faces every single day: you find a promising profile, but properly evaluating it takes 5-10 minutes. Multiply that by 30-50 profiles in a sourcing session, and you've burned an entire day just reading LinkedIn.

Today you'll learn to paste a LinkedIn profile into AI and get a structured assessment — skills match, experience gaps, culture signals, red flags, and career trajectory — in about 30 seconds.

This one skill alone will save you hours every week.

Paste a LinkedIn profile into AI and get a structured candidate assessment in 30 seconds.

The copy-paste workflow

The technique is simple but powerful. Here's the step-by-step:

Step 1: Open a candidate's LinkedIn profile.

Step 2: Select all the text on their profile page — headline, about section, experience, education, skills, recommendations. Press Ctrl+A (or Cmd+A on Mac) and then Ctrl+C to copy. Don't worry about copying navigation elements or ads — AI ignores the noise.

Step 3: Open Claude or ChatGPT and paste it in with your prompt.

Step 4: Include your role requirements alongside the profile. AI needs both the candidate data and the benchmark to compare against.

Here's the core prompt:

"Here's a LinkedIn profile and the role requirements. Give me a structured assessment: (1) Skills match — which requirements does this person clearly meet? (2) Experience gaps — what's missing or weak? (3) Culture signals — what does their profile suggest about how they work? (4) Red flags — anything concerning? (5) Career trajectory — where are they heading? (6) Overall recommendation — interview, maybe, or pass? Keep it concise."

That's it. Paste the profile, paste the requirements, ask for the assessment. You get a structured evaluation in seconds that would have taken you 5-10 minutes of careful reading.

Knowledge Check

Why should you include the role requirements when asking AI to assess a LinkedIn profile?

AI needs a benchmark to compare against — without role requirements, it can only describe the profile, not evaluate how well the candidate fits your specific role

It makes the AI response longer and more detailed

LinkedIn profiles don't contain enough information on their own

It helps AI find the candidate's contact information

Without role requirements, AI can only summarise what it sees. With role requirements, it can make a judgment — this person meets requirement X but lacks Y, their experience in Z is strong but in a different industry. The comparison is what turns a summary into an actionable assessment.

What AI extracts that you'd miss

When you manually scan a LinkedIn profile, you're looking at headlines and job titles. You're pattern-matching at a surface level — and you're doing it while fatigued after the 20th profile of the day.

AI reads every line and catches things you'd miss:

Hidden skills in descriptions. A candidate's title might say "Marketing Manager" but their description reveals they built and managed a team of 8, owned a budget, and led a product launch. That's closer to "Head of Marketing" experience.

Career velocity. AI spots that someone was promoted three times in four years at their current company — a signal of high performance that's easy to miss when you're scanning quickly.

Job-hopping patterns. AI identifies that a candidate has had 5 roles in 3 years and flags it. But it also distinguishes between concerning job-hopping and legitimate reasons — contract roles, company acquisitions, or a clear pattern of increasing responsibility.

Skill adjacencies. A candidate might not have the exact technology you need, but AI recognises that their experience with a closely related technology suggests they could learn quickly. "They used Azure, not AWS, but the infrastructure principles transfer directly."

Recommendation themes. If a candidate has recommendations, AI identifies recurring themes — "great communicator," "always meets deadlines," "strong under pressure" — giving you culture signals that aren't visible in the experience section.

🤖

Claude

online

Here's a LinkedIn profile for a candidate and the role I'm hiring for (Senior Backend Engineer — Python, AWS, 5+ years, team leadership experience). Give me a structured assessment. [Pastes full LinkedIn profile text showing: Software Engineer at a fintech startup for 3 years, previously Junior Developer for 2 years, Python and Django experience, some AWS, no direct team lead experience but mentored 2 interns, Computer Science degree, several recommendations mentioning reliability and problem-solving]

**Candidate Assessment: Senior Backend Engineer** **Skills Match:** - Python: Strong. 3+ years with Python/Django in production at current role - AWS: Partial. Mentioned in 2 projects but doesn't appear to be primary infrastructure responsibility - Experience level: 5 years total — meets minimum, but current title is mid-level, not senior **Experience Gaps:** - Team leadership: No direct management experience. Mentored 2 interns, which shows willingness but isn't equivalent to leading a team - AWS depth: Experience appears project-level, not infrastructure-level. May need to assess depth in interview - Scale: Current company is a startup — unclear if they've worked with systems at the scale your role requires **Culture Signals:** - Recommendations consistently mention "reliability" and "problem-solving" — suggests someone dependable who works through challenges rather than escalating - 3 years at current startup suggests loyalty and commitment — not a flight risk - Mentoring interns shows leadership interest even without formal title **Red Flags:** - None significant. The gap between their current level and your senior requirement is a stretch, not a red flag **Career Trajectory:** - Clear upward path: Junior to mid-level in 2 years, took on mentoring, chose a startup for broader exposure. This person is actively growing toward a senior role **Recommendation: Maybe — worth a screening call.** They're slightly below your ideal seniority level, but their trajectory, stability, and mentoring initiative suggest they could grow into the role. If you need someone who's already operating at senior level from day one, pass. If you can invest 3-6 months of development, this could be a strong hire.

↻ Replay conversation

Knowledge Check

AI flags that a candidate has had 5 jobs in 3 years. What should you do?

Ask the candidate to explain before even screening them

Immediately reject them — that's a clear red flag

Ignore it — job-hopping is normal now

Look deeper — AI should distinguish between concerning job-hopping and legitimate reasons like contract roles, acquisitions, or clear career progression

Context matters enormously. Five jobs in three years could mean a candidate who can't commit — or it could mean they did contract work, their companies were acquired, or they were headhunted repeatedly because they're exceptional. AI can usually distinguish between these patterns by reading the details of each role. Always look at the why, not just the what.

Comparing candidates against each other

Once you've assessed individual profiles, AI becomes even more powerful when you compare candidates side by side.

Try this prompt:

"I've assessed three candidates for my Senior Backend Engineer role. Here are their profiles. Rank them from strongest to weakest fit, explain your reasoning, and identify what each candidate uniquely brings to the table."

Paste all three profiles (or your AI-generated summaries from earlier assessments). AI will create a comparison matrix that highlights:

- Who most closely matches the technical requirements

- Who has the strongest leadership experience

- Who brings unique value the others don't

- Who carries the most risk and why

This is particularly useful when you're preparing a shortlist for a hiring manager. Instead of presenting three profiles and saying "they all look good," you present a ranked comparison with clear reasoning. Hiring managers love this — it shows strategic thinking and makes their decision easier.

The 30-second assessment workflow

Let's put this into a repeatable daily workflow:

During sourcing sessions: Open AI in one tab, LinkedIn in another. For every promising profile, copy the text, paste it into AI with your role requirements, and get an instant assessment. In the time it used to take to properly evaluate 5 profiles, you can now evaluate 30.

Batch processing: If you've built a longlist of 20 candidates, paste them into Claude in batches (Claude's large context window handles this well). Ask for a ranked assessment of all 20. In one prompt, you get a prioritised list.

Quick screening notes: Copy AI's assessment into your ATS notes for each candidate. When the hiring manager asks "why did you shortlist this person?", you have a structured answer ready — not just "their profile looked good."

Consistency: AI applies the same criteria to every candidate. No fatigue bias. No unconsciously favouring the candidate you reviewed right after your morning coffee over the one you scanned at 4pm on a Friday.

Knowledge Check

What is the biggest advantage of using AI for LinkedIn profile assessment instead of manual review?

AI can contact candidates directly

AI applies consistent criteria to every candidate without fatigue bias — and does it in seconds instead of minutes per profile

AI always makes perfect hiring decisions

AI can access private LinkedIn profiles

Human reviewers get tired. By the 30th profile, you're scanning headlines and making snap judgments. AI reads every profile with the same attention and applies the same criteria consistently. It doesn't get fatigued, it doesn't skip sections, and it doesn't unconsciously favour candidates based on irrelevant factors. That consistency, combined with speed, is the real advantage.

Important caveats

AI profile assessment is powerful, but use it wisely:

AI is a filter, not a decision-maker. Use AI to prioritise which candidates to look at more closely. The final decision to reach out, interview, or hire should always involve your human judgment.

Profiles are curated. LinkedIn profiles are what candidates want you to see. They may understate or overstate experience. AI works with what's visible — it can't verify claims.

Don't over-rely on keyword matching. A great candidate might describe their experience differently than your JD. AI is better than keyword-matching tools at understanding this, but remind it: "Don't just look for exact keyword matches. Consider equivalent experience and transferable skills."

Privacy matters. Be mindful of how you use and store AI-generated candidate assessments. Follow your company's data handling policies and relevant regulations. AI-generated notes should be treated with the same care as any other candidate data.

Tomorrow, you'll take this same skill and apply it to CVs and resumes — processing entire stacks in minutes.

👥

Day 5 Complete

"Copy a LinkedIn profile, paste it into AI with your role requirements, and get a structured assessment in 30 seconds. This one skill saves hours every week and makes your shortlists sharper."

Tomorrow — Day 6

Screening Resumes at Scale

Tomorrow you'll learn to process stacks of CVs in minutes — extracting key signals and building ranked shortlists with AI.

Continue to Day 6 →