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Make Confident Hiring Calls with This Candidate Fit Detector Prompt

Use the Five Whys technique to spot misalignment before it leads to a mis‑hire and make more confident decisions. Hire with clarity, not just instinct.

Harshita Gakkhar

5 Min Read

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Every Recruiter must use this Prompt to Predict Candidate Fit with 5x More Clarity

Most hiring mistakes don’t happen because of bad candidates. They happen because recruiters rely on intuition or surface-level checks to gauge fit, missing deeper signals related to motivation, skills alignment, or team compatibility.
This prompt helps you uncover the real reasons a candidate might not work out before you make the offer. Built on the proven Five Whys technique, it’s your secret weapon for making confident hiring decisions, minus the guesswork.

The Prompt

Act like a Root Cause Analyst, helping me evaluate candidates fit for a role. I’ve interviewed a candidate who seems off for some reason, but I’m not sure where the misalignment lies.
Begin by identifying the problem area (e.g., skill gap, low motivation, or team mismatch) based on this context:
Role: [insert job title]
Key requirements: [insert top 3 must-have skills or traits]
Team culture: [insert how the team works — e.g., fast-paced, async-first, heavy on collaboration, needs independent ownership, frequent brainstorming, etc.]
Candidate summary: [Paste a short summary of the candidate’s profile include things like communication style, career goals, work style preferences, reasons for job change, Interviewer evaluation, and any red/green flags mentioned]
Now, use the Five Whys technique to dig into the root cause. For each ‘why,’ explore the underlying issue more deeply based on the data provided. Once done, summarize the core misalignment and suggest whether the candidate is coachable, a risk worth taking, or a no-go.

Include both hard data (skills, feedback) and soft signals (motivation, communication, goals). This prompt is most effective when it can read between the lines the clearer your candidate summary, the sharper the fit prediction.

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