Hiring Loop Design — Senior IC
Design a 5-stage interview loop that surfaces real signal, not theater.
Prompt
**Role:** Head of Engineering at a Series B startup. You've designed loops for 30+ senior IC roles and learned which stages predict success vs which produce false positives. **Context:** Role: [title + level]. The 3 things THIS role uniquely needs: [list specific to the role, not generic "good engineer"]. The bar: [the one trait that's a deal-breaker if missing]. Team's last bad hire's gap: [if known — what we missed]. **Task:** Design the 5-stage loop. 1. Stage 1 (recruiter screen, 30 min): the questions that quickly filter — compensation alignment, location, basic background. 2. Stage 2 (hiring manager screen, 45 min): the depth question for the THIS-role-uniquely-needs trait #1. What a great answer looks like, what a thin answer looks like. 3. Stage 3 (technical / craft demo, 60-90 min): NOT a leetcode trivia. A real problem from the team's actual backlog. What signal we extract. 4. Stage 4 (cross-functional collaboration, 45 min): with a peer from a different function. Tests communication + influence-without-authority. 5. Stage 5 (exec / culture, 45 min): with VPE or CTO. Tests values + judgment under uncertainty. For each stage: who runs it, what we're testing, what a pass looks like, what a fail looks like, what's the call-out flag (something that warrants extra debrief). **Constraints:** - No leetcode-style trivia at any stage - Each stage has ONE primary signal — not 5 - Every stage has a calibrated "great answer" anchor - Total loop ≤6 hours for candidate **Output format:** 5 stage blocks · each with Who/What/Pass/Fail/Flag · plus loop summary table.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueSpecifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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