SkilsMVP
BootcampInterview prep

AI Mock Interview — Spec

AI Mock Interview — Spec

This document specifies how the app runs an AI-driven mock interview using the drill banks in this folder (product-sense.md, analytical-estimation.md, behavioral.md, rca-and-metrics.md). It's a behavior spec for the future app — there's no app yet — written so the drills and rubrics already in the repo are everything the AI needs.

Goal

Give the learner realistic, repeatable interview practice with structured feedback, approximating a human mock interviewer within the self-serve model. This is what turns "I read about interviews" (Topic 14) into "I've practiced them dozens of times."

Session flow

  1. Choose a mode: a single question, or a full mock (e.g., 1 product-sense + 1 analytical + 2 behavioral), optionally filtered by type or difficulty.
  2. Ask a question. The AI poses a drill's prompt verbatim and waits — it does not reveal the Framework, Model answer, or Rubric.
  3. Let the learner answer, ideally thinking aloud (typed or spoken). The AI may give one light nudge if the learner is stuck or silent (e.g., "What's the goal here, and who's the user?"), mirroring a real interviewer — but never feeds the answer.
  4. Optionally probe with a follow-up a real interviewer would ask ("Why that segment?" / "How would you measure it?").
  5. Score and give feedback against the drill's Rubric when the learner finishes.

Scoring & feedback

  • Evaluate the answer against the drill's Rubric signals and the Framework structure. Produce: an overall rating (e.g., Needs work / Solid / Strong), per-criterion notes (what was good, what was missing), and 2–3 concrete, actionable improvements.
  • Only after scoring, optionally show the Model answer as a reference — never before.
  • Be specific and kind: cite what the learner actually said. The aim is improvement, not a grade.
  • Watch for the classic misses the rubrics encode: no clarifying questions, no user/segment, no prioritization, no metric, not thinking aloud, jumping to a fix before diagnosing (RCA), or not using STAR (behavioral).

Repeat & improve loop

  • Each session draws a fresh question (avoid immediate repeats) so practice isn't memorized.
  • Track which rubric criteria the learner repeatedly misses across sessions and surface a "focus next on…" summary (e.g., "you often skip defining a success metric").
  • Tie into the program (../program.md): mock-interview checkpoints recommend specific drill types by week (e.g., behavioral early, product-sense and RCA before the job hunt).

Guardrails

  • The AI is a coach, not a gatekeeper — encourage retrying.
  • Never expose the rubric/model answer before the learner attempts the question.
  • Keep feedback grounded in the rubric; don't invent criteria.

Inputs the AI uses (already in the repo)

  • The drill prompt (the question), its Framework (ideal structure), Model answer (reference), and Rubric (evaluation signals) — from the four drill files in this folder.
  • No additional data needed; the content is the spec.