Build an AI CV Extractor: JSON Structured Output Workshop

The Workshop Goal

By the end of this workshop, you will have a prompt pipeline that:

1. →Takes raw CV/resume text as input
2. →Extracts structured candidate data into a predefined JSON schema
3. →Handles edge cases (missing data, ambiguous entries, multiple roles)
4. →Validates output and retries on failure

Step 1: Define Your Schema

Before writing any prompt, define the exact output structure you need.

Required: name, email, phone, location
Experience: company, role, duration, key achievements
Education: institution, degree, year
Skills: technical skills, soft skills, languages

Step 2: Build the Extraction Prompt

Step 3: Test with Real Examples

Step 4: Handle Edge Cases

Step 5: Validation and Retry

try {
  const data = JSON.parse(response);
} catch (e) {
  // Strip markdown fences and retry parse
  const cleaned = response.replace(/```json?\n?/g, '').replace(/```/g, '');
  const data = JSON.parse(cleaned);
}

Limitations

1. →No cross-referencing, The extractor trusts what the CV says. It cannot verify employment claims.
2. →Layout dependent, Heavily formatted CVs (columns, tables, graphics) may lose structure when converted to text.
3. →Bias risk, AI may assign higher confidence to CVs that match patterns in its training data.
4. →Privacy, Always send CV data through secure, compliant channels. Never log PII in development.

Test Your Understanding

What's Next

You have built a complete structured extraction pipeline! In the next module, you will learn advanced reasoning techniques, Chain-of-Thought and Self-Consistency, to tackle problems that require multi-step logic.

Continue to Chain-of-Thought and Self-Consistency to master AI reasoning patterns.