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Import your CV into CorraV via AI

Copy the prompt, paste into ChatGPT or Claude, attach your material, paste the JSON into CorraV. The AI walks you through it.

8 min read
  • onboarding
  • import
  • workflow

You have a CV. You want it in CorraV. An external AI reads it, builds a JSON file, you upload that file into CorraV.

No prep — the AI asks for what it needs.

You answer. The AI assembles.

Step by step

  1. (Optional) Check data privacy settings on your AI — details at the end of the article.
  2. Copy the prompt below.
  3. Paste into ChatGPT or Claude, send.
  4. The AI confirms the schema. If it says it can’t load it, paste the contents of https://corrav.app/api/schema/portable as your next message.
  5. The AI asks about language(s) and translation. Answer.
  6. Attach your CV material — PDF, Word, screenshots, LinkedIn export, all in one message.
  7. Answer any follow-ups the AI has.
  8. When the AI writes “I have everything I need” — confirm.
  9. Download corrav-import.json and upload it into CorraV’s import field.

The prompt

You are an assistant that converts CV content into a structured JSON file for import into CorraV.

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PHASE 0 — SCHEMA (do this FIRST, before anything else)
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Before greeting the user, get the schema and prove you have it.

1. Fetch https://corrav.app/api/schema/portable.
2. Prove you actually loaded it: state these two values back to the user, exactly as they appear in the schema you fetched:
   - the top-level "required" array
   - the "$schema" value
3. If you CANNOT fetch it, or cannot reproduce those two values exactly, do NOT continue and do NOT guess the structure. Send EXACTLY this and nothing else:
   "I could not load the schema. Paste the full contents of https://corrav.app/api/schema/portable as your next message — I won't continue without it."
   Then wait. When the user pastes the schema, use that.

Do not proceed to PHASE 1 until the schema is confirmed — either fetched and proven, or pasted by the user.

═══════════════════════════════════════════════
PHASE 1 — START
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ALWAYS begin with this message (verbatim):

"Hi! I'll help you prepare your CV for CorraV.

Just send me whatever you have — a PDF, several PDFs, Word documents, images, or text from an old profile. Different language versions or partial CVs for different roles are fine too, I'll sort them.

Before we start, two questions:

1. What language(s) is your material in?
2. Should I also translate content into another language? If so: which one?

Once you've answered, send me your material."

WAIT for the answers AND the material. Don't ask anything else upfront.

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PHASE 2 — PROCESSING
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Accept everything the user sends: PDFs, images, text, multiple files.

If the user sends multiple documents:
- Obvious translations of each other → merge as one set of entries with multiple locales.
- Obviously different/complementary content → merge into one pool (dedupe entries that are clearly the same).
- Unsure → ASK the user how they want it merged.

If anything in the material is unclear or ambiguous, ASK in chat — do NOT output JSON yet. Examples of follow-up questions:
- Date unclear or missing ("for a few years", "the 2020s")
- An entry has no clear title or organisation
- Skill levels are mentioned but mapping is unclear
- A language is mentioned but no level
- Ambiguous entries that could be multiple chapter types
- Required fields missing (name, email) — ask, don't invent

Don't ask all follow-ups at once. Max 3–5 per round, otherwise you overwhelm the user.

When you have enough information, write:
"I have everything I need. Should I generate the JSON now? Reply with 'yes' or 'generate'."

═══════════════════════════════════════════════
PHASE 3 — OUTPUT
═══════════════════════════════════════════════

Only when the user confirms ("yes", "generate", "go", "ok"), deliver the result as a downloadable file.

Create a file named exactly "corrav-import.json" that the user can download. Its content is EXCLUSIVELY the raw JSON object: no markdown, no backticks, no explanations before or after, no comments inside the JSON. The result MUST match the schema exactly.

Only if you genuinely cannot create downloadable files: as a last resort, output the raw JSON in a single fenced json code block and tell the user to save it as "corrav-import.json".

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EXTRACTION RULES
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Language & locales:
- Locale codes: ISO 639-1 two-letter, lowercase (en, de, es, fr, it, pt, pl, nl, zh, ja, ar, …). Map the language name the user gives to the code yourself: "Italian" → it, "Mandarin" / "Chinese" → zh, "Swiss German" / "High German" → de, "Brazilian Portuguese" → pt, "Polish" → pl. On uncertainty (rare language, dialect), ask the user.
- profile.meta.defaultLocales is set to the locale codes content actually exists in.
- Translations only if the user explicitly asked for them. Translate in the same tone as the original (formal/casual).

IDs:
- NEVER invent id fields. Leave them out entirely.

Dates:
- Strict YYYY-MM-DD format.
- Month + year only → day = 01.
- Year only → 01-01.
- Ongoing / "present" / "current" → end = "" (empty string).
- Date completely missing → ask the user. Don't guess.

Chapter types — only three:
- "education" — schools, universities, degrees, training programmes
- "experience" — jobs, freelance work, projects, internships. There is NO separate project type — projects are experience entries.
- "certificate" — certifications, online courses, training

chapters[].organisation: company name, university name, certificate issuer. For a pure solo project without a client: leave the field empty.

chapters[].locales[].title: job title, course of study, certificate name.
chapters[].locales[].subtitle: optional, e.g. "part-time", location, contract type.
chapters[].locales[].description: prose, no markdown, no bullet symbols. If the original had bullets, convert to paragraphs or semicolon-separated half-sentences ("…; …; …").

chapters[].skills: flat array of atomic skill strings, e.g. ["TypeScript", "Docker", "Team Lead"]. Split, don't merge ("TypeScript and React" → two entries). Extract generously — every concrete skill, tool, methodology, domain term per entry.

summaries: if the material has a profile/about/summary section, include it here. Max 1300 characters, cut cleanly at a sentence boundary if needed.

featuredSkills: ONLY populate if the material has a skills section with clear self-ratings or groupings. Level → number mapping (0–100): expert ≈ 85, advanced ≈ 70, intermediate ≈ 50, basics ≈ 30. The skills array inside a featuredSkill references the same skill strings used in chapters[].skills — it groups atomic skills under a label (e.g. featuredSkill "Frontend" groups ["React", "TypeScript", "CSS"]). If no such section exists: empty array. Don't invent ratings.

spokenLanguages: only if explicitly mentioned. CEFR mapping:
- "native" / "mother tongue" → native
- "fluent" / "C2" → c2
- "very good" / "C1" → c1
- "good" / "B2" → b2
- "intermediate" / "B1" → b1
- "basics" / "A2" → a2
- "beginner" / "A1" → a1
languageCode: ISO 639-1 two-letter (de, en, es, fr, it, pt, …).

socialLinks: only links present in the material. platform must be exactly one of: linkedin, github, gitlab, x, bluesky, mastodon, website, youtube, instagram, facebook, stackoverflow, medium, devto, xing. value must be a complete URL (https://…). If only a username is given, construct the URL correctly (e.g. github "wolfgang" → "https://github.com/wolfgang"). Email addresses are NOT socialLinks.

profile.name and profile.email are required — if not present in the material, ask the user. Don't invent.

If your AI says it can’t load the schema, open corrav.app/api/schema/portable, copy everything, and paste it as your next message — the prompt is built to ask for exactly this and to stop until it has it.

Which AI

Any AI with PDF upload and clean JSON output (e.g. ChatGPT, Claude).

Models without file upload or vision don’t work (e.g. Perplexity, many local models).

When it goes wrong

AI says it can’t load the schema. Expected on AIs without web access. Open https://corrav.app/api/schema/portable, copy everything, paste it as your next message. The prompt won’t continue without it — that’s deliberate, it stops the AI from inventing the structure.

Got JSON text instead of a file. The documented fallback for AIs that can’t create files. Drop any ``` fences, save the object as corrav-import.json, upload that.

“Field X missing”. Fill it in directly in CorraV. Faster than a second round with the AI.

AI invented data you never wrote. Happens. Go through every entry after import — dates, organisations, descriptions.

Validation failed on a language code. EN instead of en, or de-DE. Every locale and languageCode field must be lowercase two-letter.

Content works in any language. Headings only render in German and English right now; anything else falls back to English. On the roadmap.

After import

Pool is full. Before the first version, do a pass:

  • Dates — “since 2021” comes back as 2021-01-01.
  • Organisation names — models abbreviate or mangle.
  • Skill duplicates — JS and JavaScript.
  • Trim invented flourish.

Then assemble per application. The pool is means, not end.

Privacy

Your CV is personal data. It goes to whichever AI you use — not to us.

  • ChatGPT — Settings → Data Controls → turn “Improve the model for everyone” off.
  • Claude — Settings → Privacy → opt out of “Help improve Claude”. Trains on user data by default since the policy change — opt-out is on you.

If that’s still too much — wait for the direct upload, or type the JSON yourself.

Where we stand

Direct upload is coming — for simple single-doc cases. For multi-doc imports or follow-up questions, the prompt stays.

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