STARLprep.AI — AI Behavioral Interview Coaching Workflow

Overview

STARLprep.AI is an AI-native workflow that helps candidates convert unstructured personal experiences into structured, interview-ready STARL answers. The system combines prompt orchestration, workflow routing, and structured outputs to deliver reliable coaching without inventing metrics or requiring scheduling with a human coach.

I designed and shipped STARLprep.AI end-to-end, including the frontend experience, backend orchestration, and AI prompt logic.

Problem

Behavioral interviews reward structured storytelling, specificity, and measurable impact. However, most candidates struggle to translate raw experiences into interview-ready answers because:

• Their stories are unstructured and lack clear STARL components
• They cannot easily identify or quantify impact metrics
• Traditional coaching is expensive, slow, and difficult to scale
• Generic chatbots provide inconsistent feedback and often hallucinate metrics

This results in candidates underperforming despite having strong experiences.

Target users (ICP):

• MBA students preparing for tech roles (PMM, GTM, PM)
• Career switchers transitioning into tech
• Early-career professionals preparing for structured interviews

Jobs to be done:

• Turn a raw experience into a clear, structured interview answer
• Identify missing elements and strengthen impact