How to Build a Claude-Powered Interview Question Generator

Building a Claude-powered interview question generator means sending a structured prompt — job title, seniority, skills, interview type — to the Claude API and parsing back a clean set of questions with expected signal for each one. The core work isn't the AI call itself, it's designing a prompt that produces consistent, well-formatted output and wrapping that call in an API your hiring tool, ATS integration, or internal dashboard can actually use.

This guide walks through the whole thing: prompt structure, request format, parsing strategy, and the production details (auth, streaming, rate limits) that separate a weekend script from something your recruiting team relies on daily.

What the generator actually needs to produce

Before writing any code, define the output contract. A good interview question generator for developers or recruiters typically needs:

• Role-specific questions (e.g. "Senior Backend Engineer, Go, distributed systems")
• Difficulty tiers (junior / mid / senior, or screen / onsite / technical)
• Question types (behavioral, technical, system design, culture-fit)
• Follow-up prompts for the interviewer to use if the candidate's answer is shallow
• What a strong answer looks like, so non-technical interviewers can evaluate technical answers

That last point is what makes this genuinely useful instead of a glorified question-list generator. Claude is good at explaining why a question matters and what signal to look for — that's the part worth building the tool around.

Architecture overview

A minimal version looks like this:

1. Frontend or internal tool collects job details (title, level, skills, interview stage)
2. Backend builds a prompt from those inputs
3. Backend calls the Claude API (directly or through a proxy) and gets structured JSON back
4. Frontend renders questions, follow-ups, and evaluation notes
5. Optionally: save generated question sets per role so they're reusable across interview loops

Nothing here is exotic — the value is in the prompt and in making the API call reliable and cheap to run at scale.

Step 1: Design the prompt for structured output

Ask Claude to return JSON directly rather than freeform prose. This keeps your frontend simple and avoids regex parsing of markdown lists.

You are helping design a technical interview loop.

Generate 6 interview questions for this role:
- Title: {{role}}
- Level: {{level}}
- Focus areas: {{skills}}
- Interview stage: {{stage}}

Return ONLY valid JSON matching this shape:
{
  "questions": [
    {
      "question": "string",
      "type": "technical | behavioral | system-design | culture-fit",
      "difficulty": "junior | mid | senior",
      "follow_up": "string",
      "strong_answer_signal": "string"
    }
  ]
}

Being explicit about the schema matters more than being clever with wording. If you need stricter guarantees, tell Claude to omit any explanation and return JSON only — and validate the response with a JSON parser before trusting it downstream.

Step 2: Call the API

Here's a direct call using the Claude Messages API shape:

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Generate 6 interview questions for a Senior Backend Engineer focused on distributed systems..."}
    ]
  }'

If you're building this as a feature inside a product rather than a personal script, you'll quickly run into the operational side: per-team API keys, usage tracking per customer, and rate limiting so one team's bulk generation doesn't starve another. That's exactly the gap SubToAPI fills — it turns your Claude access into a normal HTTPS API with application keys (sub_live_...), so each team or workspace in your product gets its own key and usage metadata without you building billing and quota logic yourself. See the quickstart for the setup.

With SubToAPI, the same request looks like this:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Generate 6 interview questions for a Senior Backend Engineer..."}
    ]
  }'

Full request/response details are in the Messages docs.

Step 3: Parse and validate the response

Always validate before rendering. Claude is reliable with JSON instructions, but your code shouldn't assume it:

const res = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
    "content-type": "application/json",
  },
  body: JSON.stringify({
    model: "claude-sonnet-4-5",
    max_tokens: 1024,
    messages: [{ role: "user", content: prompt }],
  }),
});

const data = await res.json();
let parsed;
try {
  parsed = JSON.parse(data.content[0].text);
} catch (err) {
  // fall back: ask Claude to retry with a reminder to return JSON only
  throw new Error("Invalid JSON from model");
}

renderQuestions(parsed.questions);

A simple retry-on-parse-failure loop (one retry, with a stricter reminder appended to the prompt) handles the rare malformed response without adding real complexity.

Step 4: Add role and difficulty targeting

The generator becomes genuinely useful when it's parameterized well. Expose these as real inputs in your UI or API payload rather than baking them into one static prompt:

• Role taxonomy — map job titles to skill tags so "Staff Engineer" and "Senior Backend Engineer" don't produce identical question sets
• Interview stage — a phone screen needs broader, faster questions; an onsite loop needs depth
• Company context — optionally let teams add their own tech stack or values so questions feel specific, not generic

Store the generated sets keyed by role + stage so repeat interviews don't regenerate from scratch, and so interviewers calibrate against the same question bank.

Step 5: Stream output for a better UX

For longer question sets (10+ questions with follow-ups), streaming avoids a long blank-screen wait. SubToAPI supports streaming responses over the same endpoint — see the streaming docs for the event format. If you want the generator to also suggest coding exercises or pull from a candidate's resume as a tool call, the tools docs cover function calling patterns that fit naturally into this same request shape.

Production considerations

Once this moves from a script to a feature, plan for:

• Per-team keys instead of one shared API key, so you can track usage and shut off access per customer
• Cost control — capping max_tokens and caching question sets by role reduces redundant generation
• Seat-based access if multiple recruiters or hiring managers use the tool — see pricing for how Solo, Team, and Scale plans handle this
• Fallback behavior when the model call fails or returns malformed JSON, so the UI never shows a broken state

Sign up at /signup if you want the key management and usage tracking handled for you rather than building it from scratch.

FAQ

Can Claude generate different questions for the same role each time? Yes — by default responses vary. If you want consistent question banks per role, set a lower temperature and/or save the first generated set and reuse it rather than regenerating on every request.

How do I prevent duplicate or repetitive questions across sessions? Pass previously generated questions into the prompt and explicitly instruct Claude to avoid repeating them, or filter duplicates client-side using a simple similarity check before display.

Do I need the raw Claude API, or can I use a wrapper like SubToAPI? Either works technically. A wrapper matters once you have multiple teams, need per-customer usage data, or want streaming and tool use without building that infrastructure yourself — check the docs for the full endpoint reference.