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Module 2 - 65 min

Turn job postings into structured data

Extract fields such as role, seniority, skills, tools, and responsibilities so the assistant can reason over reliable inputs.

Fabrice Payet, co-founder of Dataaxy

Author

By Fabrice Payet, Co-founder of Dataaxy

Fabrice builds Dataaxy with the goal of helping data and AI talent turn real skills into clearer career opportunities.

Mission

Help a learner turn job text into trustworthy inputs without building Dataaxy-scale job ingestion or matching infrastructure.

Product boundary

This module teaches extraction from a user-provided job description. It does not cover scraping, enrichment pipelines, ranking, deduplication, or internal Dataaxy job-quality signals.

Objective

Create a stable data card that turns one messy job description into evidence the role-fit assistant can safely use.

Primary source

Use the prompting guide as the baseline for writing clear instructions, specifying the desired output, and iterating when results are inconsistent.

OpenAI Prompting Guide

Why structure matters

LLM output becomes more useful when the downstream product expects a shape. Instead of asking for a long free-form summary, ask for a record your application can display, compare, and test.

A clean extraction schema is also a portfolio asset. It shows that you can turn ambiguous text into product-ready data.

The goal is not to recreate a production job-ingestion system. The goal is to make a small, inspectable data card that supports one assistant response.

  • Role family and title
  • Seniority level
  • Required skills and tools
  • Core responsibilities
  • Evidence quotes from the posting
  • Uncertainty markers when the posting is vague

Keep the first schema small

Do not model the entire labor market in the first pass. Pick fields that directly support the assistant: fit scoring, gap analysis, and candidate preparation.

A smaller schema is easier to debug and easier to explain in a portfolio walkthrough.

Use a data card instead of a database

For a learning project, a data card is enough. It is a single structured object that explains how the assistant understood the role. It can live in a JSON file, a notebook cell, or a small fixture.

That constraint is healthy. It keeps the learner focused on extraction quality and prevents the lesson from drifting into job-board infrastructure.

  • Good portfolio scope: one job description in, one data card out
  • Out of scope: crawling, deduplication, ranking, alerts, recruiter dashboards
  • Quality signal: every extracted claim has text evidence or an uncertainty marker

Common mistakes

The common failure is pretending the model knows more than the job description says. If the posting does not mention remote policy, salary, or seniority, the assistant should say so.

Another failure is mixing extraction with recommendation. First produce the data card. Then let the assistant reason over it in a separate step.

  • Do not infer salary from title alone
  • Do not convert every nice-to-have into a must-have
  • Do not score candidate fit inside the extraction step
  • Do not include platform-ranking fields in the public lesson

Role-fit data card fields

  • Role title and role family
  • Seniority and confidence level
  • Must-have skills with evidence quotes
  • Nice-to-have skills with evidence quotes
  • Responsibilities grouped by work type
  • Uncertain or missing information
  • Fields intentionally excluded from the portfolio project

Retrieval practice

Answer before you continue

  1. Which fields should come directly from the job text?
  2. How should the assistant mark uncertainty instead of guessing?
  3. Which extraction fields would be too close to Dataaxy proprietary matching logic?

Exercise

Design a JSON-like role-fit data card, test it against three public AI or data job descriptions, and mark uncertain fields explicitly.

Create three role-fit data cards

Choose three public job descriptions from different data or AI roles. Convert each one into the same data card and compare the uncertain fields.

  1. Copy the job title, company, and role description into a plain text sample
  2. Extract must-have skills only when the job text gives evidence
  3. Separate responsibilities from requirements
  4. Mark seniority as explicit, inferred, or unknown
  5. Write one sentence explaining what the data card leaves out
Reveal the feedback checklist
  • Every important field is backed by a quote or marked uncertain
  • The schema works on all three job descriptions
  • The output is useful to the role-fit assistant from module 1
  • The excluded fields protect Dataaxy-level matching and marketplace logic

Artifact to ship

Role-fit data card schema and three reviewed examples

Ask for feedback

If you are working with an AI coach or mentor, ask it to review your artifact against the checklist before moving to the next module.

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