Job Interview Prep Kit

Prepare for interviews with company-specific research, behavioral story banks, technical frameworks, and salary negotiation scripts. Always delivers as an interactive web app.

When to Use

• Upcoming interview, mock question practice, salary negotiation prep, behavioral story coaching

When NOT to Use

• Resume creation (resume-maker), broad career research (deep-research)

Step 0: Ask the User — Mode Selection

Before doing anything else, ask the user:

1. What role and company are you interviewing for?
2. Which prep mode do you want?
   • Voice mock interview — AI-powered voice conversation simulating a real interview (uses OpenAI Realtime API for speech-to-speech)
   • Text mock interview — Chat-based interview simulator where the AI asks questions and gives feedback in real time
   • Interview prep dashboard — A reference website with expected questions, company intel, story frameworks, and negotiation scripts

All three modes are built as web apps deployed as artifacts. Do not output raw markdown — always build an interactive app.

Step 1: Research the Role and Company

Before building anything, use

webSearch

site:glassdoor.com [Company] interview questions [role]

site:teamblind.com [Company] interview

[Company] onsite

site:levels.fyi [Company]

site:leetcode.com/discuss [Company] [role]

[Company] [team name]

[Company] engineering blog

For Amazon specifically: each interviewer is assigned 1-3 Leadership Principles to probe. Map stories to LPs before the loop.

Step 2: Build the Web App

Mode A: Voice Mock Interview

Build a React web app that uses the OpenAI Realtime API for voice-to-voice interview simulation. The app should:

• Connect to OpenAI's Realtime API via WebSocket for low-latency speech-to-speech
• Configure the AI persona as an interviewer for the specific role/company
• Seed the system prompt with the researched questions from Step 1
• Include a question queue panel showing upcoming questions (behavioral, technical, situational) drawn from Glassdoor/Blind research
• Show a live transcript panel so the user can review what they said
• After each answer, provide written feedback on: STAR structure, specificity, quantification, and areas to improve
• Include a timer per question (recommended 2-3 min per behavioral, 15-20 min per system design)
• End with a scorecard summarizing performance across categories

// OpenAI Realtime API connection pattern
const pc = new RTCPeerConnection();
const audioEl = document.createElement("audio");
audioEl.autoplay = true;

// Add local audio track for user's microphone
const ms = await navigator.mediaDevices.getUserMedia({ audio: true });
pc.addTrack(ms.getTracks()[0]);
pc.ontrack = (e) => { audioEl.srcObject = e.streams[0]; };

// Connect to OpenAI Realtime
const tokenRes = await fetch("/api/realtime-session", { method: "POST" });
const { client_secret } = await tokenRes.json();
const offer = await pc.createOffer();
await pc.setLocalDescription(offer);

const sdpRes = await fetch("https://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${client_secret.value}`,
    "Content-Type": "application/sdp",
  },
  body: offer.sdp,
});
await pc.setRemoteDescription({ type: "answer", sdp: await sdpRes.text() });

The backend route (

/api/realtime-session

const r = await fetch("https://api.openai.com/v1/realtime/sessions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-4o-realtime-preview",
    voice: "verse",
    instructions: `You are an interviewer for [Role] at [Company]. Ask behavioral and technical questions one at a time. After the candidate answers, give brief feedback, then move to the next question. Questions: [insert researched questions from Step 1]`,
  }),
});
const data = await r.json();
// Return data.client_secret to the frontend

Mode B: Text Mock Interview

Build a React chat app that simulates a text-based interview. The app should:

• Present a chat interface styled like a messaging app
• AI asks one question at a time from the researched question bank
• After each user response, AI provides inline feedback (STAR structure check, specificity score, improvement suggestions)
• Include a sidebar with: question categories (behavioral / technical / situational), progress tracker, tips for the current question type
• Use the OpenAI Chat Completions API (not Realtime) via a backend route
• End with a summary report scoring each answer

System prompt for the interviewer AI:

You are interviewing a candidate for [Role] at [Company]. Ask questions one at a time from this list: [researched questions]. After each answer, give brief, constructive feedback focusing on: specificity, use of "I" vs "we", quantified results, and STAR structure. Then ask the next question. Be professional but conversational.

Mode C: Interview Prep Dashboard

Build a polished single-page React app with these sections:

1. Company Intel Panel — loop structure, recent news (from web search), team info, culture notes
2. Question Bank — categorized tabs (Behavioral, Technical, System Design, Culture Fit) with actual questions from Glassdoor/Blind research. Each question expandable to show:
   • Why they ask this
   • Framework to use (STAR, CAR, SOAR, STAR+L)
   • Example strong answer structure
3. Story Builder — interactive 6-story matrix (Leadership, Conflict, Failure, Ambiguity, Impact, Scope Creep). User fills in their STAR components; the app validates completeness and flags missing quantification
4. Comp & Negotiation — levels.fyi data, target/walk-away ranges, scripted negotiation lines
5. Questions to Ask — curated list with context on what each reveals
6. Countdown Timer — if the user has an interview date, show days remaining with a suggested daily prep schedule

Behavioral Story Frameworks

STAR is the baseline. Know the variants:

• STAR — Situation (1-2 sent) → Task (1 sent) → Action (60% of airtime, use "I" not "we") → Result (quantified)
• CAR — Challenge → Action → Result. Tighter; better for rapid-fire rounds.
• SOAR — Situation → Obstacle → Action → Result. Use when the story's value is in what you overcame.
• STAR+L — Append Learning. Mandatory for failure questions.

Build a 6-story matrix, not a script per question. One well-told story covers 3-4 question variants.

"Tell me about yourself": Present (current role + one win) → Past (how you got here, 1 pivot) → Future (why this role is the logical next step). 90 seconds.

Technical Frameworks

System design — RADIO:

• Requirements — Functional + non-functional. Ask: scale? latency target? (~15% of time)
• Architecture — Boxes and arrows. Client, API layer, services, data stores, queues. (~20%)
• Data model — Entities, fields, relationships. (~15%)
• Interface — API contracts. REST vs GraphQL vs gRPC. (~15%)
• Optimizations — Caching, CDN, sharding, read replicas. Senior signal lives here. (~35%)

Coding pattern recognition: ~75% of LeetCode-style questions reduce to: sliding window, two pointers, BFS/DFS, binary search on answer, heap for top-K, DP (1D/2D), union-find, monotonic stack.

Salary Negotiation

1. Never give a number first. "I'd want to learn more about the role before discussing comp — what range did you have budgeted?"
2. Only negotiate after a yes. Once they want to hire you.
3. BATNA is everything. Competing offer > current job > nothing.
4. Anchor with data. levels.fyi for exact company + level + location. Quote 75th percentile.
5. Negotiate the package, not the base. Signing bonus, equity refresh, start date, remote days, title.
6. The reframe script: "I'm really excited about this. Based on levels.fyi data for [level] at [company], I was expecting something closer to [X]. Is there flexibility?"
7. Silence is a tool. State your number. Stop talking.
8. Get it in writing.

Questions to Ask Interviewers

• "What's the one thing that, if it goes wrong in the next 6 months, keeps you up at night?"
• "What did the last person who was great in this role do differently?"
• "What's something the team tried recently that didn't work?"
• "How are decisions made when engineering and product disagree?"
• To the hiring manager: "What would make you say 'I'm so glad we hired them' at the 6-month mark?"

Best Practices

1. 6 stories, not 30 answers — depth beats breadth
2. Quantify or it didn't happen — "cut p99 from 340ms to 45ms" > "improved performance"
3. Practice out loud — written answers don't survive contact with your mouth
4. "I don't have a great example" is fine — better than a weak story. Pivot: "The closest I have is..."
5. Failure questions: own it fully — hedging is the most common disqualifier

Limitations

• Voice mode requires user to provide their OpenAI API key (for Realtime API access)
• Comp data lags reality by ~3-6 months; cross-reference Blind for recency
• Company-specific intel quality depends on how much employees post publicly
• Cannot access internal question banks or recruiter portals